{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/sentence/papers/102","list_of":"/task/sentence","task":"Sentence","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":102,"pages_in_order":108,"rows_per_page":100,"rows":[10101,10200],"of":10752,"counts":{"archive_papers_tagged":10752,"with_a_code_link":3811,"where_syntology_ran_a_sample":657,"not_listed_spam_title":0,"listed":10752,"listed_where_code_ran":657,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":544,"every_run_a_failure_of_syntologys_instrument":113,"listed_with_a_run_with_no_instrument_failure":544,"listed_every_run_a_failure_of_syntologys_instrument":113,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/sentence","prev":"/task/sentence/papers/101","next":"/task/sentence/papers/103","papers":[{"url":null,"slug":"acquiring-opposition-relations-among-italian","title":"Acquiring Opposition Relations among Italian Verb Senses using Crowdsourcing","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-core-scientific-concepts-to-context","title":"Applying Core Scientific Concepts to Context-Based Citation Recommendation","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-sentiment-analysis-in-hindi","title":"Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"axolotl-a-web-accessible-parallel-corpus-for","title":"Axolotl: a Web Accessible Parallel Corpus for Spanish-Nahuatl","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"building-a-case-based-semantic-english","title":"Building A Case-based Semantic English-Chinese Parallel Treebank","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"building-a-corpus-of-errors-and-quality-in","title":"Building a Corpus of Errors and Quality in Machine Translation: Experiments on Error Impact","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"building-the-macedonian-croatian-parallel","title":"Building the Macedonian-Croatian Parallel Corpus","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"construction-and-analysis-of-a-large","title":"Construction and Analysis of a Large Vietnamese Text Corpus","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"construction-of-an-english-dependency-corpus","title":"Construction of an English Dependency Corpus incorporating Compound Function Words","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-validating-image-description-datasets","title":"Cross-validating Image Description Datasets and Evaluation Metrics","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"crowdsourcing-a-multi-lingual-speech-corpus","title":"Crowdsourcing a Multi-lingual Speech Corpus: Recording, Transcription and Annotation of the CrowdIS Corpora","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-word-usage-errors-in-chinese","title":"Detecting Word Usage Errors in Chinese Sentences for Learning Chinese as a Foreign Language","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dt-neg-tutorial-dialogues-annotated-for","title":"DT-Neg: Tutorial Dialogues Annotated for Negation Scope and Focus in Context","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-corilga-introducing-the-automatic","title":"Enhanced CORILGA: Introducing the Automatic Phonetic Alignment Tool for Continuous Speech","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-alternative-translations-in-a-large","title":"Finding Alternative Translations in a Large Corpus of Movie Subtitle","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"first-steps-towards-coverage-based-sentence","title":"First Steps Towards Coverage-Based Sentence Alignment","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"happy-accident-a-sentiment-composition","title":"Happy Accident: A Sentiment Composition Lexicon for Opposing Polarity Phrases","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-annotation-of-sentence","title":"Improving the Annotation of Sentence Specificity","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-the-asian-language-treebank-alt","title":"Introducing the Asian Language Treebank (ALT)","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-the-lcc-metaphor-datasets","title":"Introducing the LCC Metaphor Datasets","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"managing-linguistic-and-terminological","title":"Managing Linguistic and Terminological Variation in a Medical Dialogue System","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"metonymy-analysis-using-associative-relations","title":"Metonymy Analysis Using Associative Relations between Words","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-the-spoken-wikipedia-for-speech-data","title":"Mining the Spoken Wikipedia for Speech Data and Beyond","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-annotation-in-scientific-articles","title":"Multi-label Annotation in Scientific Articles - The Multi-label Cancer Risk Assessment Corpus","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilevel-annotation-of-agreement-and","title":"Multilevel Annotation of Agreement and Disagreement in Italian News Blogs","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-elicitation-and-annotation-schemes-for","title":"Novel elicitation and annotation schemes for sentential and sub-sentential alignments of bitexts","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parallel-sentence-extraction-from-comparable","title":"Parallel Sentence Extraction from Comparable Corpora with Neural Network Features","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"phrase-level-segmentation-and-labelling-of","title":"Phrase Level Segmentation and Labelling of Machine Translation Errors","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"poly-gretel-cross-lingual-example-based","title":"Poly-GrETEL: Cross-Lingual Example-based Querying of Syntactic Constructions","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-layer-of-the-valence-dictionary-of","title":"Semantic Layer of the Valence Dictionary of Polish Walenty","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-relation-extraction-with-semantic","title":"Semantic Relation Extraction with Semantic Patterns Experiment on Radiology Reports","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-automatically-alignment-of-predicates","title":"Semi-automatically Alignment of Predicates between Speech and OntoNotes data","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semreldata-a-multilingual-contextual","title":"SemRelData ― Multilingual Contextual Annotation of Semantic Relations between Nominals: Dataset and Guidelines","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-similarity-based-on-dependency-tree","title":"Sentence Similarity based on Dependency Tree Kernels for Multi-document Summarization","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiframes-a-resource-for-verb-centered","title":"Sentiframes: A Resource for Verb-centered German Sentiment Inference","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentiment-lexicons-for-arabic-social-media","title":"Sentiment Lexicons for Arabic Social Media","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-sentence-boundary-detection-and","title":"Simultaneous Sentence Boundary Detection and Alignment with Pivot-based Machine Translation Generated Lexicons","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"specialising-paragraph-vectors-for-text","title":"Specialising Paragraph Vectors for Text Polarity Detection","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"syntax-based-multi-system-machine-translation","title":"Syntax-based Multi-system Machine Translation","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-alaskan-athabascan-grammar-database","title":"The Alaskan Athabascan Grammar Database","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-royal-society-corpus-from-uncharted-data","title":"The Royal Society Corpus: From Uncharted Data to Corpus","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-building-semantic-role-labeler-for","title":"Towards Building Semantic Role Labeler for Indian Languages","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-comparability-of-linguistic-graph","title":"Towards Comparability of Linguistic Graph Banks for Semantic Parsing","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-dependencies-for-persian","title":"Universal Dependencies for Persian","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-contextual-information-for-machine","title":"Using Contextual Information for Machine Translation Evaluation","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-word-embeddings-to-translate-named","title":"Using Word Embeddings to Translate Named Entities","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"verblexpor-a-lexical-resource-with-semantic","title":"VerbLexPor: a lexical resource with semantic roles for Portuguese","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wags-a-beautiful-english-italian-benchmark","title":"WAGS: A Beautiful English-Italian Benchmark Supporting Word Alignment Evaluation on Rare Words","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improved-system-for-sentence-level-novelty","title":"An Improved System for Sentence-level Novelty Detection in Textual Streams","date":"2016-04-30","arxiv_id":"1605.00122","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-fifty-natural-languages-and-twelve","title":"Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance","date":"2016-04-28","arxiv_id":"1604.08561","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-and-how-to-pay-different-attention-to","title":"Why and How to Pay Different Attention to Phrase Alignments of Different Intensities","date":"2016-04-23","arxiv_id":"1604.06896","repositories_listed":0,"syntology":null},{"url":null,"slug":"word2visualvec-image-and-video-to-sentence","title":"Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Prediction","date":"2016-04-23","arxiv_id":"1604.06838","repositories_listed":0,"syntology":null},{"url":null,"slug":"dialog-based-language-learning","title":"Dialog-based Language Learning","date":"2016-04-20","arxiv_id":"1604.06045","repositories_listed":0,"syntology":null},{"url":null,"slug":"cognitive-state-classification-using","title":"Cognitive state classification using transformed fMRI data","date":"2016-04-19","arxiv_id":"1604.05413","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-coherence","title":"Inductive Coherence","date":"2016-04-18","arxiv_id":"1604.05288","repositories_listed":0,"syntology":null},{"url":null,"slug":"acd-action-concept-discovery-from-image","title":"ACD: Action Concept Discovery from Image-Sentence Corpora","date":"2016-04-16","arxiv_id":"1604.04784","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-level-grammatical-error","title":"Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction","date":"2016-04-16","arxiv_id":"1604.04677","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-matrix-factorization-for-cross","title":"Supervised Matrix Factorization for Cross-Modality Hashing","date":"2016-04-16","arxiv_id":"1603.05572","repositories_listed":0,"syntology":null},{"url":null,"slug":"attributes-as-semantic-units-between-natural","title":"Attributes as Semantic Units between Natural Language and Visual Recognition","date":"2016-04-12","arxiv_id":"1604.03249","repositories_listed":0,"syntology":null},{"url":"/paper/improving-sentence-compression-by-learning-to","slug":"improving-sentence-compression-by-learning-to","title":"Improving sentence compression by learning to predict gaze","date":"2016-04-12","arxiv_id":"1604.03357","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-learning-of-local-structured","title":"Semi-supervised learning of local structured output predictors","date":"2016-04-11","arxiv_id":"1604.03010","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-sentence-level-lstm-language-models-for","title":"Using Sentence-Level LSTM Language Models for Script Inference","date":"2016-04-11","arxiv_id":"1604.02993","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-structured-scene-parsing-by-learning","title":"Deep Structured Scene Parsing by Learning with Image Descriptions","date":"2016-04-08","arxiv_id":"1604.02271","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-headline-generation-with-sentence-wise","title":"Neural Headline Generation with Sentence-wise Optimization","date":"2016-04-07","arxiv_id":"1604.01904","repositories_listed":0,"syntology":null},{"url":null,"slug":"resolving-language-and-vision-ambiguities","title":"Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes","date":"2016-04-07","arxiv_id":"1604.02125","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-level-recurrent-topic-model-letting","title":"Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves","date":"2016-04-07","arxiv_id":"1604.02038","repositories_listed":0,"syntology":null},{"url":"/paper/a-corpus-and-evaluation-framework-for-deeper","slug":"a-corpus-and-evaluation-framework-for-deeper","title":"A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories","date":"2016-04-06","arxiv_id":"1604.01696","repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-relational-information-in-question","title":"Modeling Relational Information in Question-Answer Pairs with Convolutional Neural Networks","date":"2016-04-05","arxiv_id":"1604.01178","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-narrative-texts-punctuation-marks-obey-the","title":"In narrative texts punctuation marks obey the same statistics as words","date":"2016-04-04","arxiv_id":"1604.00834","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-compositional-approach-to-language-modeling","title":"A Compositional Approach to Language Modeling","date":"2016-04-01","arxiv_id":"1604.00100","repositories_listed":0,"syntology":null},{"url":null,"slug":"attsum-joint-learning-of-focusing-and","title":"AttSum: Joint Learning of Focusing and Summarization with Neural Attention","date":"2016-04-01","arxiv_id":"1604.00125","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-sentence-relation-modeling-with","title":"Enhancing Sentence Relation Modeling with Auxiliary Character-level Embedding","date":"2016-03-30","arxiv_id":"1603.09405","repositories_listed":0,"syntology":null},{"url":null,"slug":"model-interpolation-with-trans-dimensional","title":"Model Interpolation with Trans-dimensional Random Field Language Models for Speech Recognition","date":"2016-03-30","arxiv_id":"1603.09170","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-readable-read-automatic-assessment-of","title":"A Readable Read: Automatic Assessment of Language Learning Materials based on Linguistic Complexity","date":"2016-03-29","arxiv_id":"1603.08868","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-single-document-summarization","title":"Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints","date":"2016-03-29","arxiv_id":"1603.08887","repositories_listed":0,"syntology":null},{"url":null,"slug":"prepositional-attachment-disambiguation-using","title":"Prepositional Attachment Disambiguation Using Bilingual Parsing and Alignments","date":"2016-03-29","arxiv_id":"1603.08594","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-visual-explanations","title":"Generating Visual Explanations","date":"2016-03-28","arxiv_id":"1603.08507","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-you-see-what-i-mean-visual-resolution-of","title":"Do You See What I Mean? Visual Resolution of Linguistic Ambiguities","date":"2016-03-26","arxiv_id":"1603.08079","repositories_listed":0,"syntology":null},{"url":null,"slug":"pointing-the-unknown-words","title":"Pointing the Unknown Words","date":"2016-03-26","arxiv_id":"1603.08148","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-semantic-models-with-word-sentence","title":"Evaluating semantic models with word-sentence relatedness","date":"2016-03-23","arxiv_id":"1603.07253","repositories_listed":0,"syntology":null},{"url":"/paper/a-graph-kernel-based-on-context-vectors-for","slug":"a-graph-kernel-based-on-context-vectors-for","title":"A graph kernel based on context vectors for extracting drug–drug interactions","date":"2016-03-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-convolutional-neural-networks-to","title":"Comparing Convolutional Neural Networks to Traditional Models for Slot Filling","date":"2016-03-16","arxiv_id":"1603.05157","repositories_listed":0,"syntology":null},{"url":null,"slug":"multichannel-variable-size-convolution-for","title":"Multichannel Variable-Size Convolution for Sentence Classification","date":"2016-03-15","arxiv_id":"1603.04513","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-learning-templates-and-slots-for-event","title":"Joint Learning Templates and Slots for Event Schema Induction","date":"2016-03-04","arxiv_id":"1603.01333","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgnc-cnn-a-simple-approach-to-exploiting","title":"MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification","date":"2016-03-03","arxiv_id":"1603.00968","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantifying-the-vanishing-gradient-and-long","title":"Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs","date":"2016-03-01","arxiv_id":"1603.00423","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-and-predicting-tag-importance-for","title":"Measuring and Predicting Tag Importance for Image Retrieval","date":"2016-02-28","arxiv_id":"1602.08680","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-word-prediction-in-bangla-language","title":"Automated Word Prediction in Bangla Language Using Stochastic Language Models","date":"2016-02-25","arxiv_id":"1602.07803","repositories_listed":0,"syntology":null},{"url":null,"slug":"petrarch-2-petrarcher","title":"Petrarch 2 : Petrarcher","date":"2016-02-23","arxiv_id":"1602.07236","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-lstm-clstm-models-for-large-scale","title":"Contextual LSTM (CLSTM) models for Large scale NLP tasks","date":"2016-02-19","arxiv_id":"1602.06291","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-comparative-study-of-word-and","title":"A Comprehensive Comparative Study of Word and Sentence Similarity Measures","date":"2016-02-17","arxiv_id":"1610.04533","repositories_listed":0,"syntology":null},{"url":null,"slug":"paraphrase-generation-from-latent-variable","title":"Paraphrase Generation from Latent-Variable PCFGs for Semantic Parsing","date":"2016-01-22","arxiv_id":"1601.06068","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-spoken-document-summarization-with","title":"Improved Spoken Document Summarization with Coverage Modeling Techniques","date":"2016-01-20","arxiv_id":"1601.05194","repositories_listed":0,"syntology":null},{"url":null,"slug":"graded-entailment-for-compositional","title":"Graded Entailment for Compositional Distributional Semantics","date":"2016-01-19","arxiv_id":"1601.04908","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-scarcity-of-crossing-dependencies-a","title":"The scarcity of crossing dependencies: a direct outcome of a specific constraint?","date":"2016-01-13","arxiv_id":"1601.03210","repositories_listed":0,"syntology":null},{"url":null,"slug":"trans-gram-fast-cross-lingual-word-embeddings","title":"Trans-gram, Fast Cross-lingual Word-embeddings","date":"2016-01-11","arxiv_id":"1601.02502","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-sentence-level-information-with","title":"Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling","date":"2016-01-07","arxiv_id":"1601.01530","repositories_listed":0,"syntology":null},{"url":"/paper/msp-improv-an-acted-corpus-of-dyadic","slug":"msp-improv-an-acted-corpus-of-dyadic","title":"MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception","date":"2016-01-07","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-entropy-a-new-evaluation-metric","title":"Contrastive Entropy: A new evaluation metric for unnormalized language models","date":"2016-01-03","arxiv_id":"1601.00248","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-approach-for-single-text-document","title":"Hybrid Approach for Single Text Document Summarization using Statistical and Sentiment Features","date":"2016-01-03","arxiv_id":"1601.00643","repositories_listed":0,"syntology":null}],"record_sha256":"70eccaaa6f74aace2e3ee39724fd536748b4ff187a97d7e58a820cd5d63ffef7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}