CSS-LM

1
[{"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": "The classical << MLE reestimation algorithms >> , namely the forward-backward algorithm and the [[ segmental k-means algorithm ]] , are expanded and reestimation formulas are given for HMM with Gaussian mixture observation densities .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "We show that the suggested hybrid proba-bilistic model -LRB- which combines global variables , like translation , with << local variables >> , like [[ relative positions ]] and appearances of body parts -RRB- , leads to : -LRB- i -RRB- faster convergence of learning phase , -LRB- ii -RRB- robustness to occlusions , and , -LRB- iii -RRB- higher recognition rate .", "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": "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": "HYPONYM-OF", "sentence": "Currently several << grammatical formalisms >> converge towards being declarative and towards utilizing context-free phrase-structure grammar as a backbone , e.g. [[ LFG ]] and PATR-II .", "aspect": "scii"}, {"sentiment": "PART-OF", "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": "PART-OF", "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": "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": "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": "COMPARE", "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": "COMPARE", "sentence": "The experimental results demonstrate that the proposed [[ method ]] makes it possible to significantly improve speech quality in the converted singing voice while preserving the conversion accuracy of singer identity compared to the conventional << SVC >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "To validate our method , we compare [[ it ]] with the << Maximum Likelihood -LRB- ML -RRB- estimation method >> under sparse data and with the Expectation Maximization -LRB- EM -RRB- algorithm under incomplete data respectively .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "While the [[ model ]] is more complex than << those >> which have been employed for unsupervised learning of POS tags in English , which use only syntactic information , the variety of languages in the world requires that we consider morphology as well .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The major objective of this program is to develop and demonstrate robust , high performance [[ continuous speech recognition -LRB- CSR -RRB- techniques ]] focussed on application in << Spoken Language Systems -LRB- SLS -RRB- >> which will enhance the effectiveness of military and civilian computer-based systems .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Second , we show in this paper how a [[ lexical hierarchy ]] is used in predicting new << linguistic concepts >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "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": "USED-FOR", "sentence": "Unlike previous video relighting methods , the approach does not assume regions of uniform albedo , which makes [[ it ]] applicable to << richly textured scenes >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Our [[ approach ]] yields << phrasal and single word lexical paraphrases >> as well as syntactic paraphrases .", "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": "A key complementary objective is to define and develop applications of robust speech recognition and understanding systems , and to help catalyze the transition of spoken language technology into military and civilian systems , with particular focus on application of robust [[ CSR ]] to << mobile military command and control >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We describe the methods and [[ hardware ]] that we are using to produce a real-time demonstration of an << integrated Spoken Language System >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The experimental results demonstrated that the << annotation >> performance of this method outperforms some traditional [[ annotation methods ]] by about 8 % in mean precision , showing a potential of the Maximum Entropy Model in the task of automatic image annotation .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We argue that a more sophisticated and fine-grained annotation in the tree-bank would have very positve effects on stochastic parsers trained on the tree-bank and on grammars induced from the treebank , and it would make the [[ treebank ]] more valuable as a source of data for << theoretical linguistic investigations >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We present an << approach >> to annotating a level of discourse structure that is based on identifying [[ discourse connectives ]] and their arguments .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "This paper describes a particular << approach >> to parsing that utilizes recent advances in [[ unification-based parsing ]] and in classification-based knowledge representation .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We incorporate this analysis into a [[ diagnostic tool ]] intended for developers of << machine translation systems >> , and demonstrate how our application can be used by developers to explore patterns in machine translation output .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "Our [[ approach ]] can handle the high << intra-class variability >> and large proportion of unrelated images returned by search engines .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We solve the three << factors >> in a [[ coarse-to-fine manner ]] and achieve reliable change decision by rank minimization .", "aspect": "scii"}, {"sentiment": "USED-FOR", "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": "USED-FOR", "sentence": "<< It >> is based on the [[ theory of tenses ]] of H. Kamp and Ch .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "In this sense , [[ operations ]] on << SI-Nets >> are not merely isomorphic to single epistemological objects , but can be viewed as a simulation of processes on a different level , that pertaining to the conceptual system of NL .", "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": "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": "USED-FOR", "sentence": "In noisy conditions , the mismatch between corrupted speech signals and << models >> trained on [[ clean speech ]] may cause the decoder to produce word matches with unrealistic durations .", "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": "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": "USED-FOR", "sentence": "Experimental results in the domain of << face detection >> show the [[ training algorithm ]] yields significant improvements in performance over conventional AdaBoost .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "With WordNet , it is easy to retrieve sets of semantically related words , a facility that will be used for [[ sense resolution ]] during << text processing >> , as follows .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We present an efficient [[ algorithm ]] for the << redundancy elimination problem >> : Given an underspecified semantic representation -LRB- USR -RRB- of a scope ambiguity , compute an USR with fewer mutually equivalent readings .", "aspect": "scii"}, {"sentiment": "USED-FOR", "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": "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": "Specifically , we formulate the << conjugate prior >> in the form of [[ Bregman divergence ]] and show that it is the inherent geometry of conjugate priors that makes them appropriate and intuitive .", "aspect": "scii"}, {"sentiment": "FEATURE-OF", "sentence": "We attack an inexplicably under-explored language genre of spoken language -- lyrics in music -- via completely unsuper-vised induction of an << SMT-style stochastic transduction grammar >> for [[ hip hop lyrics ]] , yielding a fully-automatically learned challenge-response system that produces rhyming lyrics given an input .", "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": "CONJUNCTION", "sentence": "We explore a variety of predictive models , including [[ Na \u00a8 \u0131ve Bayes mixture models ]] and << mixtures of Markov models >> , and report empirical evidence that MINPATH finds useful shortcuts that save substantial navigational effort .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Performance of each investigated classifier is evaluated both via [[ receiving operating curve ]] and via a << measure >> , called mean absolute error , related to the quality in predicting the corresponding word error rate .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "Although the experiments in this article are on natural language parsing -LRB- NLP -RRB- , the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks , for example , speech recognition , [[ machine translation ]] , or << natural language generation >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "This is particularly important when building translation systems for [[ new language pairs ]] or << new domains >> .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "The main feature of this model is to view [[ parsing ]] and << generation >> as two strongly interleaved tasks performed by a single parametrized deduction process .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "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": "CONJUNCTION", "sentence": "We develop Wallflower , a three-component system for background maintenance : the [[ pixel-level component ]] performs Wiener filtering to make probabilistic predictions of the expected background ; the << region-level component >> fills in homogeneous regions of foreground objects ; and the frame-level component detects sudden , global changes in the image and swaps in better approximations of the background .", "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": "We have evaluated this << strategy >> with our [[ spoken dialogue system '' Dialogue Navigator for Kyoto City '' ]] , which also has question-answering capability .", "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": "EVALUATE-FOR", "sentence": "Extensive experiments show that our << method >> works satisfactorily on challenging [[ image data ]] , which establishes a technical foundation for solving several computer vision problems , such as motion analysis and image restoration , using the blur information .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "Following recent developments in the [[ automatic evaluation ]] of machine translation and << document summarization >> , we present a similar approach , implemented in a measure called POURPRE , for automatically evaluating answers to definition questions .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "After several experiments , and trained with a little corpus of 100,000 words , the << system >> guesses correctly not placing commas with a [[ precision ]] of 96 % and a recall of 98 % .", "aspect": "scii"}]