CSS-LM

1
[{"sentiment": "USED-FOR", "sentence": "Through two experiments , three methods for constructing word vectors , i.e. , [[ LSA-based , cooccurrence-based and dictionary-based methods ]] , were compared in terms of the ability to represent two kinds of << similarity >> , i.e. , taxonomic similarity and associative similarity .", "aspect": "scii"}, {"sentiment": "USED-FOR", "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": "USED-FOR", "sentence": "We present an image set classification algorithm based on << unsupervised clustering >> of [[ labeled training and unla-beled test data ]] where labels are only used in the stopping criterion .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The inclusion of these illumination constraints allows for better << recovery of shading and textures >> by [[ inpainting ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "To avoid this oscillation , we augment the motion model with a [[ generic temporal constraint ]] which increases the robustness against competing interpretations , leading to more meaningful << content summarization >> .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We propose a << unified variational formulation >> for joint motion estimation and segmentation with [[ explicit occlusion handling ]] .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "In this paper we discuss a proposed user knowledge modeling architecture for the ICICLE system , a [[ language tutoring application ]] for << deaf learners >> of written English .", "aspect": "scii"}, {"sentiment": "USED-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": "USED-FOR", "sentence": "We develop several << blur features >> modeled by image color , [[ gradient ]] , and spectrum information , and use feature parameter training to robustly classify blurred images .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "A [[ construction-specific approach ]] also aids in << task-specific language development >> by allowing a language definition that is natural in terms of the task domain to be interpreted directly without compilation into a uniform grammar formalism , thus greatly speeding the testing of changes to the language definition .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "We use this [[ geometric understanding of conjugate priors ]] to derive the << hyperparameters >> and expression of the prior used to couple the generative and discriminative components of a hybrid model for semi-supervised learning .", "aspect": "scii"}, {"sentiment": "USED-FOR", "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": "USED-FOR", "sentence": "We investigate the utility of an [[ algorithm ]] for translation lexicon acquisition -LRB- SABLE -RRB- , used previously on a very large corpus to acquire << general translation lexicons >> , when that algorithm is applied to a much smaller corpus to produce candidates for domain-specific translation lexicons .", "aspect": "scii"}, {"sentiment": "USED-FOR", "sentence": "The present paper focusses on << terminology structuring >> by [[ lexical methods ]] , which match terms on the basis on their content words , taking morphological variants into account .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "We address the problem of populating object category detection datasets with dense , per-object 3D reconstructions , bootstrapped from class labels , [[ ground truth figure-ground segmentations ]] and a small set of << keypoint annotations >> .", "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": "The system participated in all the tracks of the segmentation bakeoff -- PK-open , [[ PK-closed ]] , << AS-open >> , AS-closed , HK-open , HK-closed , MSR-open and MSR - closed -- and achieved the state-of-the-art performance in MSR-open , MSR-close and PK-open tracks .", "aspect": "scii"}, {"sentiment": "CONJUNCTION", "sentence": "In this paper , we compare the relative effects of [[ segment order ]] , << segmentation >> and segment contiguity on the retrieval performance of a translation memory system .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "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": "EVALUATE-FOR", "sentence": "In this paper , we compare the relative effects of segment order , segmentation and segment contiguity on the [[ retrieval ]] performance of a << translation memory system >> .", "aspect": "scii"}, {"sentiment": "EVALUATE-FOR", "sentence": "The [[ NormF ]] of the best summary and that of the fixed summary for << categorization task >> are 0.4090 and 0.4023 .", "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": "For this purpose , we have designed a version of << KL-ONE >> which represents the [[ epistemological level ]] , while the new experimental language , KL-Conc , represents the conceptual level .", "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": "In << Bayesian machine learning >> , [[ conjugate priors ]] are popular , mostly due to mathematical convenience .", "aspect": "scii"}, {"sentiment": "HYPONYM-OF", "sentence": "The Interval Algebra -LRB- IA -RRB- and a subset of the << Region Connection Calculus -LRB- RCC -RRB- >> , namely [[ RCC-8 ]] , are the dominant Artificial Intelligence approaches for representing and reasoning about qualitative temporal and topological relations respectively .", "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": "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": "COMPARE", "sentence": "Third , [[ artificial neural networks ]] tend to outperform << support vector regression >> .", "aspect": "scii"}, {"sentiment": "COMPARE", "sentence": "We validate the effectiveness of the proposed [[ joint filter ]] through extensive comparisons with << state-of-the-art methods >> .", "aspect": "scii"}]