CSS-LM

1
[{"sentiment": "USED-FOR", "sentence": "The strength of our approach is that it allows a tree to be represented as an arbitrary set of features , without concerns about how these features interact or overlap and without the need to define a derivation or a << generative model >> which takes these [[ features ]] into account .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This [[ referential information ]] is vital for resolving zero pronouns and improving << machine translation outputs >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "To make the annotation less subjective and more reliable , recent studies employ << crowdsourcing tools >> to collect pairwise comparisons -- relying on [[ majority voting ]] to prune the annotation outliers/errors .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Using a state-of-the-art Chinese word sense disambiguation model to choose [[ translation candidates ]] for a typical << IBM statistical MT system >> , we find that word sense disambiguation does not yield significantly better translation quality than the statistical machine translation system alone .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "A << tensor method >> tuned with carefully optimized [[ derivative filters ]] yields reliable and dense displacement vector fields -LRB- DVF -RRB- with an accuracy of up to a few hundredth pixels/frame for real-world images .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This paper presents a [[ formal analysis ]] for a large class of words called << alternative markers >> , which includes other -LRB- than -RRB- , such -LRB- as -RRB- , and besides .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We introduce a new [[ method ]] for the << reranking task >> , based on the boosting approach to ranking problems described in Freund et al. -LRB- 1998 -RRB- .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The << answering agents >> adopt fundamentally different [[ strategies ]] , one utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We propose a solution to the challenge of the CoNLL 2008 shared task that uses a [[ generative history-based latent variable model ]] to predict the most likely derivation of a << synchronous dependency parser >> for both syntactic and semantic dependencies .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The recognizer makes use of [[ continuous density HMM ]] with Gaussian mixture for << acoustic modeling >> and n-gram statistics estimated on the newspaper texts for language modeling .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The << generalized LR parsing >> is enhanced in this [[ approach ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This paper proposes a << method >> for learning joint embed-dings of images and text using a [[ two-branch neural network ]] with multiple layers of linear projections followed by nonlinearities .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This << problem >> has been previously analyzed in -LRB- de Swart , 1991 -RRB- as an instance of the proportion problem and given a solution from a [[ Generalized Quantifier approach ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "During late-2013 through early-2014 NIST coordinated a special << i-vector challenge >> based on data used in previous [[ NIST Speaker Recognition Evaluations -LRB- SREs -RRB- ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The [[ method ]] amounts to tagging << LMs >> with confidence measures and picking the best hypothesis corresponding to the LM with the best confidence .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "First , we transform the original instances into a new data representation using << projections >> learnt from [[ pairwise constraints ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "In particular , we explore the capacity and limitations of << statistical learning mechanisms >> that have recently gained prominence in [[ cognitive psychology ]] and linguistics .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This [[ mining procedure ]] of << AND and OR patterns >> is readily integrated to boosting , which improves the generalization ability over the conventional boosting decision trees and boosting decision stumps .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Previous [[ change detection methods ]] , focusing on << detecting large-scale significant changes >> , can not do this well .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We describe three techniques for making << syntactic analysis >> more robust -- an agenda-based scheduling parser , a [[ recovery technique ]] for failed parses , and a new technique called terminal substring parsing .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This paper describes to what extent deep processing may benefit from shallow techniques and it presents a << NLP system >> which integrates a linguistic PoS tagger and chunker as a preprocessing module of a [[ broad coverage unification based grammar of Spanish ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "It describes the automated training and evaluation of an Optimal Position Policy , a << method >> of locating the likely positions of topic-bearing sentences based on [[ genre-specific regularities of discourse structure ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We present a [[ single-image highlight removal method ]] that incorporates illumination-based constraints into << image in-painting >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Thereby a << system of relevant times >> provided by the [[ preceeding text ]] and by the temporal adverbials of the sentence being processed is used .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "In our experiments , we use [[ Wallstreet Journal -LRB- WSJ -RRB- data ]] to train a << recognizer >> , which is adapted and evaluated in the Phonebook domain .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "With a [[ sentence-aligned corpus ]] , << translation equivalences >> are suggested by analysing the frequency profiles of parallel concordances .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Furthermore , << word-based collocational properties >> can be obtained through an [[ auxiliary module of automatic segmentation ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We are the first to bring the [[ closed form solution ]] to such a very practical << problem >> arising in video surveillance .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We show some applications of this << solution >> to additional temporal anaphora phenomena in [[ quantified sentences ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Thus a [[ personality-aware coupled reconstruction loss ]] is utilized to learn the << dictionaries >> based on face pairs from neighboring age groups .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We show that the newly proposed concept-distance measures outperform traditional [[ distributional word-distance measures ]] in the << tasks >> of -LRB- 1 -RRB- ranking word pairs in order of semantic distance , and -LRB- 2 -RRB- correcting real-word spelling errors .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We describe a [[ multi-tagging approach ]] which maintains a suitable level of lexical category ambiguity for accurate and efficient << ccg parsing >> .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Since wordbreaks are not conventionally marked in Chinese text corpora , a << character-based collocation system >> has the dual advantages of avoiding pre-processing distortion and directly [[ accessing sub-lexical information ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "We provide a detailed preliminary analysis of << inter-annotator agreement >> - both the [[ level of agreement ]] and the types of inter-annotator variation .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "On this basis , we discuss the problems of [[ vagueness ]] and ambiguity in << semantic annotation >> .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "This paper proposes that sentence analysis should be treated as defeasible reasoning , and presents such a treatment for Japanese sentence analyses using an argumentation system by Konolige , which is a formalization of defeasible reasoning , that includes << arguments >> and defeat rules that capture [[ defeasibility ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "For a large family of penalized empirical risk minimization problems , our methods exploit [[ data dependent local smoothness ]] of the << loss functions >> near the optimum , while maintaining convergence guarantees .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous << lexico-syntactic knowledge >> about [[ SCFs ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "The proposed << scheme >> eliminates redundant copying while maintaining the [[ quasi-destructive scheme 's ability ]] to avoid over copying and early copying combined with its ability to handle cyclic structures without algorithmic additions .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Due to the capacity of pan-tilt-zoom -LRB- PTZ -RRB- cameras to simultaneously cover a panoramic area and maintain high resolution imagery , researches in << automated surveillance systems >> with multiple [[ PTZ cameras ]] have become increasingly important .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "For << mobile speech application >> , [[ speaker DOA estimation accuracy ]] , interference robustness and compact physical size are three key factors .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Typically the processing of these << formalisms >> is organized within a [[ chart-parsing framework ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "This study compares the effect of [[ noise ]] and reverberation on << depression prediction >> using 1 -RRB- standard mel-frequency cepstral coefficients -LRB- MFCCs -RRB- , and 2 -RRB- features designed for noise robustness , damped oscillator cepstral coefficients -LRB- DOCCs -RRB- .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "The advantage of this novel method is that it clusters all [[ inflected forms ]] of an << ambiguous word >> in one classifier , therefore augmenting the training material available to the algorithm .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Implementation and empirical results are described for the the analysis of [[ dependency structures ]] of << Japanese patent claim sentences >> .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "However most of the works found in the literature have focused on identifying and understanding << temporal expressions >> in [[ newswire texts ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "To overcome this limitation , we propose a novel mapping algorithm that derives the [[ relative positioning ]] and orientation between two << PTZ cameras >> based on a unified polynomial model .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Projective reconstruction refers to a determination of the [[ 3D geometrical configuration ]] of a set of << 3D points and cameras >> , given only correspondences between points in the images .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "<< Speech recognition >> experiments in [[ simulated and real reverberant environments ]] show the efficiency of our approach which outperforms standard channel normaliza-tion techniques .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "On a subset of the most difficult SENSEVAL-2 nouns , the accuracy difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that << manually sense-tagged data >> have in their [[ sense coverage ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "A << declarative formalism >> is presented which permits [[ direct mappings of one feature structure into another ]] , and illustrative examples are given of its application to areas of current interest .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "An implementation on << devices >> that are highly portable but have [[ limited computational resources ]] would greatly contribute to its practical use .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "These theoretical results show that one can intrinsically segment a << piece-wise planar scene >> from [[ 2-D images ]] without explicitly performing any 3-D reconstruction .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "A parser MDCC is presented which implements an augmented Friedman - Warren algorithm permitting post referencing * and interfaces with a language of intenslonal logic translator LILT so as to display the << derivational history >> of corresponding [[ reduced IL formulae ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "The network is trained using a << large-margin objective >> that combines cross-view ranking constraints with [[ within-view neighborhood structure preservation constraints ]] inspired by metric learning literature .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "The format of the << corpus >> adopts the [[ Child Language Data Exchange System -LRB- CHILDES -RRB- ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Based on the geometrization of class structures , optimizing class structures in the feature space is equivalent to maximizing the << quadratic quantities of metric tensors >> in the [[ semi-Riemannian space ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Experimental results show that our << method >> has permitted autonomous , stable and effective [[ information integration ]] to construct the internal model of hierarchical perceptual sounds .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "We show that intrinsic image methods can be used to refine an initial , low-frequency shading estimate based on a << global lighting reconstruction >> from an original [[ texture and coarse scene geometry ]] in order to resolve the inherent global ambiguity in shading .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "One of the major scientific goals of SmartKom is to design new computational methods for the seamless << integration and mutual disambiguation of multimodal input and output >> on a [[ semantic and pragmatic level ]] .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "We first study the impact of the GNSS noise inflation on the << covariance >> of the [[ EKF outputs ]] so as to compute a least square estimate of the potential variance jumps .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Thus the work reported addresses two [[ robustness problems ]] faced by current experimental << natural language processing systems >> : coping with an incomplete lexicon and with incomplete knowledge of phrasal constructions .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "In a motorized vehicle a number of easily measurable signals with [[ frequency components ]] related to the << rotational speed of the engine >> can be found , e.g. , vibrations , electrical system voltage level , and ambient sound .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Robustness to outliers is evaluated on two << real-world tasks >> related to [[ speech segmentation ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Experimental results demonstrate that our proposed << algorithm >> presents substantially reduced [[ computational complexity ]] and improved flexibility at the cost of slightly decreased pixel accuracy , as compared with the work of Chen and Wang .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "The classification performance of the << learning algorithms >> was estimated using the [[ face database ]] with the true gender of the faces as labels , and also with the gender estimated by the subjects .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "<< Robustness >> to outliers is evaluated on two [[ real-world tasks ]] related to speech segmentation .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "<< Intra-sentential quality >> is evaluated with [[ rule-based heuristics ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "This mining procedure of AND and OR patterns is readily integrated to boosting , which improves the [[ generalization ability ]] over the conventional boosting decision trees and << boosting decision stumps >> .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "To implement the two speech enhancement systems based on real-time VC , one from NAM to a whispered voice and the other from electrolaryngeal speech to a natural voice , we propose several << methods >> for reducing [[ computational cost ]] while preserving conversion accuracy .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "The experiments show that our algorithm outperforms state-of-the-art << point set registration algorithms >> on many [[ quantitative metrics ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Recently considerable progress has been made by a number of groups involved in the DARPA Spoken Language Systems -LRB- SLS -RRB- program to agree on a methodology for comparative evaluation of SLS systems , and that [[ methodology ]] has been put into practice several times in comparative tests of several << SLS systems >> .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Since the significance of words differs in IR , << automatic speech recognition -LRB- ASR -RRB- >> performance has been evaluated based on [[ weighted word error rate -LRB- WWER -RRB- ]] , which gives a weight on errors from the viewpoint of IR , instead of word error rate -LRB- WER -RRB- , which treats all words uniformly .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "In experiments using the [[ Penn WSJ corpus ]] , our automatically trained << model >> gave a performance of 86.6 % -LRB- F1 , sentences < 40 words -RRB- , which is comparable to that of an unlexicalized PCFG parser created using extensive manual feature selection .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Using only 40 utterances from the target speaker for << adaptation >> , the [[ error rate ]] dropped to 4.1 % -- a 45 % reduction in error compared to the SI result .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "The performance of the << algorithm >> is verified on [[ noise-free and noisy data ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "We show that our << method >> can greatly speed up the training time for stochastic attention networks in the domains of image classification and [[ caption generation ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "<< It >> also gets a precision of 70 % and a [[ recall ]] of 49 % in the task of placing commas .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "In such domains a cascade of simple << classifiers >> each trained to achieve high detection rates and [[ modest false positive rates ]] can yield a final detector with many desirable features : including high detection rates , very low false positive rates , and fast performance .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Experimentally we show that our new << approaches >> lead to improved performance on both [[ image segmentation tasks ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "When used as pre-training for action recognition , << our method >> gives significant gains over learning without external data on [[ benchmark datasets ]] like UCF101 and HMDB51 .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "The accuracy rate of [[ syntactic disambiguation ]] is raised from 46.0 % to 60.62 % by using this novel << approach >> .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "For both [[ corpora ]] << word recognition >> experiments were carried out with vocabularies containing up to 20k words .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Over two distinct datasets , we find that indexing according to simple << character bigrams >> produces a [[ retrieval accuracy ]] superior to any of the tested word N-gram models .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "We conclude by showing how our design can provide a rich and robust information base to a language assessment / correction application by modeling << user proficiency >> at a high level of [[ granularity ]] and specificity .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "In this paper , we evaluate the application of these << segmen-tation algorithms >> to large vocabulary speech recognition using [[ statistical n-gram language models ]] based on the proposed word segments instead of entire words .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "To the best of our knowledge , << the proposed method >> is the first one to achieve this on both deliberate as well as [[ spontaneous facial affect data ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "We have recently reported on two new word-sense disambiguation systems , << one >> trained on [[ bilingual material ]] -LRB- the Canadian Hansards -RRB- and the other trained on monolingual material -LRB- Roget 's Thesaurus and Grolier 's Encyclopedia -RRB- .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Through experiments with parallel corpora of a Korean and English language pairs , we show that our paraphrasing method effectively extracts paraphrases with high precision , 94.3 % and 84.6 % respectively for Korean and English , and the << translation knowledge >> extracted from the bilingual corpora could be generalized successfully using the paraphrases with the 12.5 % [[ compression ratio ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "New experimental results on all four [[ applications ]] are provided to show the effectiveness of the << MAP estimation approach >> .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Our experiments demonstrate that our << approach >> yields much more accurate [[ 3D reconstructions ]] than state-of-the-art techniques .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "In experiments using the [[ Penn WSJ corpus ]] , our automatically trained model gave a performance of 86.6 % -LRB- F1 , sentences < 40 words -RRB- , which is comparable to that of an << unlexicalized PCFG parser >> created using extensive manual feature selection .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Further , in their optimum configuration , bag-of-words methods are shown to be equivalent to << segment order-sensitive methods >> in terms of [[ retrieval accuracy ]] , but much faster .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "By extensive experiments , we show that our << learned representation >> can significantly boost several [[ video recognition tasks ]] -LRB- retrieval , classification , and highlight detection -RRB- over traditional video representations .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "A larger << model >> trained after the deadline achieves 80.5 % macro-average F1 , 87.6 % syntactic dependencies LAS , and 73.1 % [[ semantic dependencies F1 ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "We applied the proposed << method >> to question classification and sentence alignment tasks to evaluate its performance as a similarity measure and a [[ kernel function ]] .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "In a previous paper -LRB- Carl , 2007 -RRB- we have described how the hypotheses graph is generated through [[ shallow mapping ]] and << permutation rules >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "In this paper , we extend traditional linear FKT to enable it to work in [[ multi-class problem ]] and also in << higher dimensional -LRB- kernel -RRB- subspaces >> and therefore provide enhanced discrimination ability .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "We use a maximum likelihood criterion to train a log-linear block bigram model which uses [[ real-valued features ]] -LRB- e.g. a language model score -RRB- as well as << binary features >> based on the block identities themselves , e.g. block bigram features .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "In cross-domain learning , there is a more challenging problem that the domain divergence involves more than one dominant factors , e.g. , different [[ viewpoints ]] , various << resolutions >> and changing illuminations .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Diagrams are common tools for representing complex concepts , [[ relationships ]] and << events >> , often when it would be difficult to portray the same information with natural images .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "However , for ultra-wide baselines , as in the case of aerial images captured under large camera rotations , the appearance variation goes beyond the reach of [[ SIFT ]] and << RANSAC >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "This study compares the effect of [[ noise ]] and << reverberation >> on depression prediction using 1 -RRB- standard mel-frequency cepstral coefficients -LRB- MFCCs -RRB- , and 2 -RRB- features designed for noise robustness , damped oscillator cepstral coefficients -LRB- DOCCs -RRB- .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "This paper proposes that sentence analysis should be treated as defeasible reasoning , and presents such a treatment for Japanese sentence analyses using an argumentation system by Konolige , which is a formalization of defeasible reasoning , that includes [[ arguments ]] and << defeat rules >> that capture defeasibility .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Some of the principles which are relevant to the topic of this paper are : -LRB- a -RRB- Multiple Layer of Grammars -LRB- b -RRB- Multiple Layer Presentation -LRB- c -RRB- [[ Lexicon Driven Processing ]] -LRB- d -RRB- << Form-Oriented Dictionary Description >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "For mobile speech application , speaker DOA estimation accuracy , [[ interference robustness ]] and << compact physical size >> are three key factors .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four speech recognition applications , namely parameter smoothing , [[ speaker adaptation ]] , << speaker group modeling >> and corrective training .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "This system consists of one or more reference times and temporal perspective times , the [[ speech time ]] and the << location time >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "The compact description of a video sequence through a single image map and a dominant motion has applications in several domains , including video browsing and retrieval , compression , [[ mosaicing ]] , and << visual summarization >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "The recognizer makes use of [[ continuous density HMM ]] with << Gaussian mixture >> for acoustic modeling and n-gram statistics estimated on the newspaper texts for language modeling .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "We detail the [[ computational complexity ]] and << average retrieval times >> for looking up phrase translations in our suffix array-based data structure .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "This paper presents an algorithm for labeling curvilinear structure at multiple scales in [[ line drawings ]] and << edge images >> Symbolic CURVE-ELEMENT tokens residing in a spatially-indexed and scale-indexed data structure denote circular arcs fit to image data .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "By using [[ commands ]] or << rules >> which are defined to facilitate the construction of format expected or some mathematical expressions , elaborate and pretty documents can be successfully obtained .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "The idea behind our method is to utilize certain [[ layout structures ]] and << linguistic pattern >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "The method combined the [[ log-likelihood ]] under a << baseline model >> -LRB- that of Collins -LSB- 1999 -RSB- -RRB- with evidence from an additional 500,000 features over parse trees that were not included in the original model .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "The resulting model attains [[ simultaneous dynamic recognition ]] and << intensity estimation of facial expressions >> of multiple emotions .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Constraints provided by observed [[ pixel colors ]] , << highlight color analysis >> and illumination color uniformity are employed in our method to improve estimation of the underlying diffuse color .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Unfortunately , such estimates would typically require the relations -LRB- scale factors -RRB- between the [[ frequency components ]] and the << speed >> for different gears to be known .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "The recognizer makes use of [[ continuous density HMM ]] with Gaussian mixture for acoustic modeling and << n-gram statistics >> estimated on the newspaper texts for language modeling .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Next , we provide baseline results on the animated GIF description task , using three representative techniques : [[ nearest neighbor ]] , << statistical machine translation >> , and recurrent neural networks .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "We further show the usefulness of dormant independencies in [[ model testing ]] and << induction >> by giving an algorithm that uses constraints entailed by dormant independencies to prune extraneous edges from a given causal graph .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "First , images are partitioned into regions using << one-class classification >> and [[ patch-based clustering algorithms ]] where one-class classifiers model the regions with relatively uniform color and texture properties , and clustering of patches aims to detect structures in the remaining regions .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Furthermore , we introduce global variables in the model , which can represent global properties such as [[ translation ]] , << scale >> or viewpoint .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Each of these parsing strategies exploits different types of knowledge ; and their combination provides a strong framework in which to process conjunctions , [[ fragmentary input ]] , and << ungrammatical structures >> , as well as less exotic , grammatically correct input .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Acoustic modeling uses [[ cepstrum-based features ]] , << context-dependent phone models -LRB- intra and interword -RRB- >> , phone duration models , and sex-dependent models .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the [[ relative positioning ]] and << orientation >> among multiple PTZ cameras .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Next , we provide baseline results on the animated GIF description task , using three representative techniques : nearest neighbor , [[ statistical machine translation ]] , and << recurrent neural networks >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Each of these parsing strategies exploits different types of knowledge ; and their combination provides a strong framework in which to process [[ conjunctions ]] , << fragmentary input >> , and ungrammatical structures , as well as less exotic , grammatically correct input .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "[[ Statistical machine translation -LRB- SMT -RRB- ]] is currently one of the hot spots in << natural language processing >> .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "The use of a KL-ONE style representation for parsing and semantic interpretation was first explored in the PSI-KLONE system -LSB- 2 -RSB- , in which parsing is characterized as an << inference process >> called [[ incremental description refinement ]] .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We show that the model trained on a certain type of data , e.g. , RGB and depth images , generalizes well for other << modalities >> , e.g. , [[ Flash/Non-Flash and RGB/NIR images ]] .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Yet , they are scarcely used for the assessment of << language pairs >> like [[ English-Chinese ]] or English-Japanese , because of the word segmentation problem .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Representing images with layers has many important << applications >> , such as [[ video compression ]] , motion analysis , and 3D scene analysis .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "As evidence of its usefulness and usability , it has been used successfully in a research context to uncover relationships between language and behavioral patterns in two distinct << domains >> : tutorial dialogue -LRB- Kumar et al. , submitted -RRB- and [[ on-line communities ]] -LRB- Arguello et al. , 2006 -RRB- .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Within the EU Network of Excellence PASCAL , a challenge was organized to design a statistical machine learning algorithm that segments words into the << smallest meaning-bearing units of language >> , [[ morphemes ]] .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We have recently reported on two new << word-sense disambiguation systems >> , one trained on bilingual material -LRB- the Canadian Hansards -RRB- and the [[ other ]] trained on monolingual material -LRB- Roget 's Thesaurus and Grolier 's Encyclopedia -RRB- .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four << speech recognition applications >> , namely parameter smoothing , [[ speaker adaptation ]] , speaker group modeling and corrective training .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Extensive experiments in common << applications >> such as [[ 2D/3D image segmentations ]] and 3D surface fitting demonstrate the effectiveness of our approach .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "To implement the two << speech enhancement systems >> based on real-time VC , one from NAM to a whispered voice and the [[ other ]] from electrolaryngeal speech to a natural voice , we propose several methods for reducing computational cost while preserving conversion accuracy .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "The research effort focusses on developing advanced [[ acoustic modelling ]] , rapid search , and recognition-time adaptation techniques for robust large-vocabulary CSR , and on applying these << techniques >> to the new ARPA large-vocabulary CSR corpora and to military application tasks .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "For solving this problem a novel optimization scheme , called Priority-BP , is proposed which carries two very important << extensions >> over standard belief propagation -LRB- BP -RRB- : '' [[ priority-based message scheduling ]] '' and '' dynamic label pruning '' .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Because of its adaptive nature , Bayesian learning serves as a unified approach for the following four << speech recognition applications >> , namely parameter smoothing , speaker adaptation , [[ speaker group modeling ]] and corrective training .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We show that the model trained on a certain type of << data >> , e.g. , [[ RGB and depth images ]] , generalizes well for other modalities , e.g. , Flash/Non-Flash and RGB/NIR images .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "For this purpose , a file card model of discourse model and knowledge store is introduced enabling the decomposition and formal representation of its determination process as a << programmable algorithm >> -LRB- [[ FDA ]] -RRB- .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We evaluate our approach on several standard << datasets >> such as im2gps , San Francisco and [[ MediaEval2010 ]] , and obtain state-of-the-art results .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "It is achieved by deriving the inter-sensor data ratio model of an AVS in bispectrum domain -LRB- BISDR -RRB- and exploring the << favorable properties >> of bispectrum , such as zero value of Gaussian process and different [[ distribution of speech and NSI ]] .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "After introducing this approach to MT system design , and the basics of [[ monolingual UCG ]] , we will show how the << two >> can be integrated , and present an example from an implemented bi-directional English-Spanish fragment .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Representing images with layers has many important << applications >> , such as video compression , motion analysis , and [[ 3D scene analysis ]] .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "The suggested approach combines multiple << cues >> , i.e. , positions , velocities and [[ appearance ]] into both the learning and detection phases .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Many promising experimental results on the << real datasets >> including ImageCLEF and [[ Me-diaMill ]] demonstrate the effectiveness and efficiency of the proposed algorithm .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "In this paper we present a statistical profile of the [[ Named Entity task ]] , a specific << information extraction task >> for which corpora in several languages are available .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Many promising experimental results on the << real datasets >> including [[ ImageCLEF ]] and Me-diaMill demonstrate the effectiveness and efficiency of the proposed algorithm .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We have recently reported on two new word-sense disambiguation systems , one trained on bilingual material -LRB- the Canadian Hansards -RRB- and the other trained on << monolingual material >> -LRB- Roget 's Thesaurus and [[ Grolier 's Encyclopedia ]] -RRB- .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Third , we consider another alternative method rarely used for building a << dictionary >> : an English-to-Korean dictionary and [[ English-to-Japanese dictionary ]] .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We have recently reported on two new << word-sense disambiguation systems >> , [[ one ]] trained on bilingual material -LRB- the Canadian Hansards -RRB- and the other trained on monolingual material -LRB- Roget 's Thesaurus and Grolier 's Encyclopedia -RRB- .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "An alternative index could be the << activity >> such as discussing , planning , [[ informing ]] , story-telling , etc. .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "The two << evaluation measures >> of the [[ BLEU score ]] and the NIST score demonstrated the effect of using an out-of-domain bilingual corpus and the possibility of using the language model .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Experiments show that the learned tracker performs much better than existing trackers on the tracking of << complex non-rigid motions >> such as [[ fish twisting ]] with self-occlusion and large inter-frame lip motion .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "A method of sense resolution is proposed that is based on WordNet , an on-line lexical database that incorporates << semantic relations >> -LRB- synonymy , antonymy , [[ hyponymy ]] , meronymy , causal and troponymic entailment -RRB- as labeled pointers between word senses .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We find that simple << interpolation methods >> , like [[ log-linear and linear interpolation ]] , improve the performance but fall short of the performance of an oracle .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "In contrast to existing [[ methods ]] that consider only the guidance image , our << method >> can selectively transfer salient structures that are consistent in both guidance and target images .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "The [[ method ]] overcomes the limitations of conventional << statistical methods >> which require large corpora to be effective , and lexical approaches which depend on existing bilingual dictionaries .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "The << method >> outperforms its state-of-the-art [[ counterparts ]] in both accuracy and scalability , especially when it comes to the retrieval of small , rotated objects .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "In our experiments , the [[ method ]] achieves a TRDR score that is significantly higher than that of the << baseline >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Extensive experiments show the superior performance of our [[ approach ]] over state-of-the-art << change detection methods >> and its ability to distinguish real scene changes from false ones caused by lighting variations .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "We experimentally investigate the functioning of Ant-Q and we show that the results obtained by [[ Ant-Q ]] on symmetric TSP 's are competitive with those obtained by other << heuristic approaches >> based on neural networks or local search .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Since product analysis is a generalization of factor analysis , [[ product analysis ]] always finds a higher data likelihood than << factor analysis >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "A consequence of that framework was a proposal for a new approach to the segmentation of complex scenes into regions corresponding to [[ coherent surfaces ]] rather than merely << regions of similar color >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Compared to the 2012 [[ SRE ]] , the << i-vector challenge >> saw an increase in the number of participants by nearly a factor of two , and a two orders of magnitude increase in the number of systems submitted for evaluation .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "First and most notably : standard << MFCC features >> suffer dramatically under test/train mismatch for both noise and reverberation ; [[ DOCC features ]] are far more robust .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "The performance of [[ SRDA ]] is tested on face recognition -LRB- singular case -RRB- and handwritten capital letter classification -LRB- nonsingular case -RRB- against existing << algorithms >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "In this paper , we propose a feasible process of such a transfer , comparing the possibilities the << Praguian dependency-based approach >> offers with the [[ Penn discourse annotation ]] based primarily on the analysis and classification of discourse connectives .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Over two distinct datasets , we find that indexing according to simple [[ character bigrams ]] produces a retrieval accuracy superior to any of the tested << word N-gram models >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Compared with previous [[ model-based approaches ]] , our << approach >> has the following advantages .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "The results demonstrates that the [[ classifier ]] based on SAE detects the ASR errors better than the other << classification methods >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Experiments show that this [[ approach ]] is superior to a single << decision-tree classifier >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "In experimental evaluation , our proposed [[ method ]] outperforms previous << shift-reduce dependency parsers >> for the Chine language , showing improvement of dependency accuracy by 10.08 % .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Experimental results on the Olympic Sports and UCF101 datasets demonstrate that the proposed attribute-based representation can significantly boost the performance of [[ action recognition algorithms ]] and outperform most recently proposed << recognition approaches >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "These methods can not be easily applied to [[ data ]] larger than the << memory capacity >> due to the random access to the disk .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Experimental evaluations with two other state of the art << extraction systems >> indicate that the [[ IntEx system ]] achieves better performance without the labor intensive pattern engineering requirement .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Further , in their optimum configuration , [[ bag-of-words methods ]] are shown to be equivalent to << segment order-sensitive methods >> in terms of retrieval accuracy , but much faster .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Our experiments with IARPA-Babel languages show that << bottleneck features >> trained on the most similar source language perform better than [[ those ]] trained on all available source languages .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "First , instead of using the model space as a [[ regular-izer ]] , we directly use it as our << search space >> , thus resulting in a more elegant formulation with fewer unknowns and fewer equations .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Experimental results are presented to compare [[ LNMF ]] with the << NMF and PCA methods >> for face representation and recognition , which demonstrates advantages of LNMF .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "This new [[ algorithm ]] deviates from the traditional << approach of wall building and layering >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Finally , a cross-corpus -LRB- and cross-language -RRB- experiment reveals better noise and reverberation robustness for [[ DOCCs ]] than for << MFCCs >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Later , however , Breiman cast serious doubt on this explanation by introducing a boosting algorithm , [[ arc-gv ]] , that can generate a higher margins distribution than << AdaBoost >> and yet performs worse .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "This [[ approach ]] differs from other << approaches >> to WSI in that it enhances the effect of the one sense per collocation observation by using triplets of words instead of pairs .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "However , speech quality of the [[ converted singing voice ]] is significantly degraded compared to that of a << natural singing voice >> due to various factors , such as analysis and modeling errors in the vocoder-based framework .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "We investigated whether [[ automatic phonetic transcriptions -LRB- APTs -RRB- ]] can replace << manually verified phonetic transcriptions >> -LRB- MPTs -RRB- in a large corpus-based study on pronunciation variation .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "While this [[ task ]] has much in common with << paraphrases acquisition >> which aims to discover semantic equivalence between verbs , the main challenge of entailment acquisition is to capture asymmetric , or directional , relations .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "We show that [[ SHORTSTR2 ]] is complementary to the existing algorithms SHORTGAC and << HAGGISGAC >> that exploit short supports , while being much simpler .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Each << generalized metaphor >> contains a recognition network , a basic mapping , additional [[ transfer mappings ]] , and an implicit intention component .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "The proposed << mechanism >> includes title-driven name recognition , [[ adaptive dynamic word formation ]] , identification of 2-character and 3-character Chinese names without title .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Unlike the quantitative prior , the qualitative prior is often ignored due to the difficulty of incorporating [[ them ]] into the << model learning process >> .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Thirdly the learned [[ intrinsic object structure ]] is integrated into a << particle-filter style tracker >> .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Specifically , the following << components >> of the system are described : the syntactic analyzer , based on a Procedural Systemic Grammar , the [[ semantic analyzer ]] relying on the Conceptual Dependency Theory , and the dictionary .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "This paper shows how the process of fitting a lexicalized grammar to a domain can be automated to a great extent by using a << hybrid system >> that combines traditional [[ knowledge-based techniques ]] with a corpus-based approach .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Experiments are described and powerful training techniques are demonstrated that permit decision-making by the [[ connectionist component ]] in the << parsing process >> .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "The features of the [[ definition sentences ]] in the << dictionary >> , the mechanical extraction of the hierarchical relations and the estimation of the results are discussed .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "In this paper , we describe the [[ pronominal anaphora resolution module ]] of << Lucy >> , a portable English understanding system .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Motivated by these arguments , we introduce a number of new << performance enhancing techniques >> including part of speech tagging , new similarity measures and [[ expanded stop lists ]] .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Some of the << principles >> which are relevant to the topic of this paper are : -LRB- a -RRB- Multiple Layer of Grammars -LRB- b -RRB- Multiple Layer Presentation -LRB- c -RRB- Lexicon Driven Processing -LRB- d -RRB- [[ Form-Oriented Dictionary Description ]] .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "We use a convex formulation of the multi-label Potts model with label costs and show that the [[ asymmetric map-uniqueness criterion ]] can be integrated into our << formulation >> by means of convex constraints .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "In this paper , we propose a partially-blurred-image classification and analysis framework for automatically detecting << images >> containing [[ blurred regions ]] and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Although Bikel 's parser achieves a higher accuracy for parsing written language , it achieves a higher accuracy when extracting [[ subcategorization cues ]] from << spoken language >> .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "This paper also shows how these [[ principles ]] are realized in the current << system >> .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Different from previous studies , we propose an approximate phrase mapping algorithm and incorporate the [[ mapping score ]] into the << correlation measure >> .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Our approach is based on the use of a << relational probability model >> to define a generative model for the domain , including models of author and title corruption and a [[ probabilistic citation grammar ]] .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "First , starting from the WSJ-trained recognizer , how much [[ adaptation data ]] -LRB- taken from the << Phonebook training corpus >> -RRB- is necessary to achieve a reasonable recognition performance in spite of the high degree of mismatch ?", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "Our << algorithm >> considers [[ chordal QCNs ]] and a new form of partial consistency which we define as \u25c6 G-consistency .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "We built a novel , extensive << dataset >> on geometric context of video to evaluate our method , consisting of over 100 ground-truth [[ annotated outdoor videos ]] with over 20,000 frames .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "In cross-domain learning , there is a more challenging problem that the << domain divergence >> involves more than one [[ dominant factors ]] , e.g. , different viewpoints , various resolutions and changing illuminations .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "We propose a process model for hierarchical perceptual sound organization , which recognizes [[ perceptual sounds ]] included in << incoming sound signals >> .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "This paper proposes an automatic , essentially << domain-independent means of evaluating Spoken Language Systems -LRB- SLS -RRB- >> which combines software we have developed for that purpose -LRB- the '' Comparator '' -RRB- and a set of [[ specifications ]] for answer expressions -LRB- the '' Common Answer Specification '' , or CAS -RRB- .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "This paper presents an approach to reliably extracting layers from images by taking advantages of the fact that homographies induced by [[ planar patches ]] in the << scene >> form a low dimensional linear subspace .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "This paper describes the detailed construction of the [[ transfer phase ]] of our << system >> from Japanese to English , and gives some examples of problems which seem difficult to treat in the interlingual approach .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "The << board >> plugs directly into the VME bus of the SUN4 , which controls the system and contains the [[ natural language system ]] and application back end .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "In this paper , we present << Photogeometric Structured Light >> whereby a standard structured light method is extended to include [[ photometric methods ]] .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "A << parsing algorithm >> is presented that integrates several different [[ parsing strategies ]] , with case-frame instantiation dominating .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "We show that AS is a particular instance of the Ant-Q family , and that there are [[ instances ]] of this << family >> which perform better than AS .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "This mining procedure of [[ AND and OR patterns ]] is readily integrated to << boosting >> , which improves the generalization ability over the conventional boosting decision trees and boosting decision stumps .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "[[ Background maintenance ]] is a frequent element of << video surveillance systems >> .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "A deterministic parser is under development which represents a departure from traditional deterministic parsers in that << it >> combines both [[ symbolic and connectionist components ]] .", "aspect": "scii"}]