CSS-LM

1
[{"sentiment": "COMPARE", "sentence": "Contrary to existing [[ greedy techniques ]] , these << tasks >> are posed in the form of a discrete global optimization problem with a well defined objective function .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "Third , [[ artificial neural networks ]] tend to outperform << support vector regression >> .", "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 compare the [[ lexically-induced relations ]] with the original << MeSH relations >> : after a quantitative evaluation of their congruence through recall and precision metrics , we perform a qualitative , human analysis ofthe ` new ' relations not present in the MeSH .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "For classification , the brain needs more processing for stimuli close to that [[ hyperplane ]] than for << those >> further away .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We introduce a new << interactive corpus exploration tool >> called [[ InfoMagnets ]] .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We compare two wide-coverage << lexicalized grammars of English >> , [[ LEXSYS ]] and XTAG , finding that the two grammars exploit EDOL in different ways .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "Currently , there are two << dominant approaches >> : the first approximates the Expected-IoU -LRB- EIoU -RRB- score as Expected-Intersection-over-Expected-Union -LRB- EIoEU -RRB- ; and the [[ second approach ]] is to compute exact EIoU but only over a small set of high-quality candidate solutions .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "However , for grammar formalisms which use more << fine-grained grammatical categories >> , for example tag and [[ ccg ]] , tagging accuracy is much lower .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "[[ Interpreting metaphors ]] is an integral and inescapable process in << human understanding of natural language >> .", "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": "In this paper , we present a << fully automated extraction system >> , named [[ IntEx ]] , to identify gene and protein interactions in biomedical text .", "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": "USED-FOR", "sentence": "This commonality suggests that some of the [[ classification-based representation techniques ]] can be applied to << unification-based linguistic descriptions >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "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": "USED-FOR", "sentence": "We describe a [[ nonlinear generalization of factor analysis ]] , called `` product analy-sis '' , that models the << observed variables >> as a linear combination of products of normally distributed hidden variables .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We present a << framework >> for word alignment based on [[ log-linear models ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The goal of this paper is to discover a set of [[ discriminative patches ]] which can serve as a fully << unsupervised mid-level visual representation >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Furthermore , a problem of [[ forming articulatory trajectories ]] is formulated to solve << labial coarticulation effects >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "In this paper , for the first time , we propose to use a << Neural Network classifier >> furnished by an [[ SAE structure ]] for detecting the errors made by a strong Automatic Speech Recognition -LRB- ASR -RRB- system .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Traditional << information retrieval techniques >> use a [[ histogram of keywords ]] as the document representation but oral communication may offer additional indices such as the time and place of the rejoinder and the attendance .", "aspect": "scii"}, {"sentiment": "USED-FOR", "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": "USED-FOR", "sentence": "We derive a [[ convex optimization problem ]] for the task of << segmenting sequential data >> , which explicitly treats presence of outliers .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We also present a prototype concordancer , CARE , which exploits the [[ move-tagged abstracts ]] for << digital learning >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The speech-search algorithm is implemented on a board with a single [[ Intel i860 chip ]] , which provides a factor of 5 speed-up over a SUN 4 for << straight C code >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "In order to boost the translation quality of << EBMT >> based on a small-sized bilingual corpus , we use an out-of-domain bilingual corpus and , in addition , the [[ language model ]] of an in-domain monolingual corpus .", "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": "We motivate our << model design >> by citing relevant research on [[ second language and cognitive skill acquisition ]] , and briefly discuss preliminary empirical evidence supporting the design .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The article also introduces a new [[ algorithm ]] for the << boosting approach >> which takes advantage of the sparsity of the feature space in the parsing data .", "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": "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": "USED-FOR", "sentence": "Our algorithm can be applied to any language pairs , but for the present we focus on building a << Korean-to-Japanese dictionary >> using [[ English ]] as a pivot .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "A second << model >> then attempts to improve upon this initial ranking , using additional [[ features ]] of the tree as evidence .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The goal of this research is to develop a [[ spoken language system ]] that will demonstrate the usefulness of voice input for << interactive problem solving >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We also discuss exploitation of the [[ database ]] for working out a more adequate tagging and << lemmatization >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "In this paper , we propose a novel [[ method ]] , called local non-negative matrix factorization -LRB- LNMF -RRB- , for learning << spatially localized , parts-based subspace representation of visual patterns >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "As an [[ educational tool ]] , it has been used as part of a unit on << protocol analysis >> in an Educational Research Methods course .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The method we are currently working on uses an expectation-maximization -LRB- EM -RRB- based word-clustering algorithm , and we have evaluated the effectiveness of this << method >> using [[ Japanese verb phrases ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Robust << natural language interpretation >> requires strong semantic domain models , [[ fail-soft recovery heuristics ]] , and very flexible control structures .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "For another , [[ linguistic representation ]] used by << language processing systems >> is not geared to learning .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This paper describes a domain independent strategy for the multimedia articulation of answers elicited by a [[ natural language interface ]] to << database query applications >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This paper presents an << approach >> to the unsupervised learning of parts of speech which uses both [[ morphological and syntactic information ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "During training , the blocks are learned from << source interval projections >> using an underlying [[ word alignment ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "To further demonstrate its applications for computer vision , we apply it to learn a [[ BN model ]] for << facial Action Unit -LRB- AU -RRB- recognition >> from real image data .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This is particularly important when building [[ translation systems ]] for << new language pairs >> or new domains .", "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": "[[ Structured-light methods ]] actively generate << geometric correspondence data >> between projectors and cameras in order to facilitate robust 3D reconstruction .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This paper proposes [[ document oriented preference sets -LRB- DoPS -RRB- ]] for the << disambiguation of the dependency structure >> of sentences .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "In this paper , we introduce a closed-form solution to systematically combine the limited training data with some generic [[ qualitative knowledge ]] for << BN parameter learning >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Based on the model , a [[ music scene analysis system ]] has been developed for acoustic signals of ensemble music , which recognizes << rhythm >> , chords , and source-separated musical notes .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "In this paper , we propose a [[ learning-based approach ]] to construct a << joint filter >> based on Convolution-al Neural Networks .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The problem of << predicting image or video interestingness >> from their [[ low-level feature representations ]] has received increasing interest .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Motivated by the intuition that it often underlies the local structure of coherent text , we develop a method that discovers verb entailment using evidence about << discourse relations >> between clauses available in a [[ parsed corpus ]] .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "In this paper we formulate story link detection and new event detection as information retrieval task and hypothesize on the impact of [[ precision ]] and recall on both << systems >> .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "In this paper , we propose an [[ automatic estimation method ]] for << word significance -LRB- weights -RRB- >> based on its influence on IR .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "We present controlled experiments showing the [[ WSD accuracy ]] of current typical << SMT models >> to be significantly lower than that of all the dedicated WSD models considered .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Experiments show that the efficiency of the overall analysis improves significantly and that our << system >> also provides [[ robustness ]] to the linguistic processing while maintaining both the accuracy and the precision of the grammar .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Our evaluation on [[ videos of public squares and courtyards ]] demonstrates our effectiveness in << localizing functional objects >> and predicting people 's trajectories in unobserved parts of the video footage .", "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": "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 << models >> were evaluated in two ways : by [[ cross-validation ]] against the corpus , and by asking users to rate the output .", "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": "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": "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 this paper , we present a << corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system >> for Dutch which combines statistical classification -LRB- maximum entropy -RRB- with [[ linguistic information ]] .", "aspect": "scii"}, {"sentiment": "PART-OF", "sentence": "In this paper , we present a << corpus-based supervised word sense disambiguation -LRB- WSD -RRB- system >> for Dutch which combines statistical classification -LRB- [[ maximum entropy ]] -RRB- with linguistic information .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "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": "CONJUNCTION", "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": "CONJUNCTION", "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": "CONJUNCTION", "sentence": "This approach is sufficient for languages with little inflection such as English , but fails for highly inflective languages such as [[ Czech ]] , << Russian >> , Slovak or other Slavonic languages .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "An efficient ranking algorithm is described , together with experimental results showing significant improvements over simple [[ enumeration ]] or a << lattice-based approach >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "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": "CONJUNCTION", "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": "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": "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": "CONJUNCTION", "sentence": "[[ Deictic reference ]] and << feedback >> about the discourse are enabled .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "This approach is sufficient for << languages >> with little [[ inflection ]] such as English , but fails for highly inflective languages such as Czech , Russian , Slovak or other Slavonic languages .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "Our results suggest that human classification can be modeled by some << hyperplane algorithms >> in the [[ feature space ]] we used .", "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": "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": "<< LPC based speech coders >> operating at [[ bit rates ]] below 3.0 kbits/sec are usually associated with buzzy or metallic artefacts in the synthetic speech .", "aspect": "scii"}]