{"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/semantic-textual-similarity/papers/17","list_of":"/task/semantic-textual-similarity","task":"Semantic Textual Similarity","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":17,"pages_in_order":24,"rows_per_page":100,"rows":[1601,1700],"of":2381,"counts":{"archive_papers_tagged":2381,"with_a_code_link":693,"where_syntology_ran_a_sample":144,"not_listed_spam_title":0,"listed":2381,"listed_where_code_ran":144,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":118,"every_run_a_failure_of_syntologys_instrument":26,"listed_with_a_run_with_no_instrument_failure":118,"listed_every_run_a_failure_of_syntologys_instrument":26,"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/semantic-textual-similarity","prev":"/task/semantic-textual-similarity/papers/16","next":"/task/semantic-textual-similarity/papers/18","papers":[{"url":null,"slug":"ecnu-at-semeval-2018-task-10-evaluating","title":"ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gkr-the-graphical-knowledge-representation","title":"GKR: the Graphical Knowledge Representation for semantic parsing","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"igevorse-at-semeval-2018-task-10-exploring-an","title":"Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ircms-at-semeval-2018-task-7-evaluating-a","title":"IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"itnlp-arc-at-semeval-2018-task-12-argument","title":"ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"meaning_space-at-semeval-2018-task-10","title":"Meaning\\_space at SemEval-2018 Task 10: Combining explicitly encoded knowledge with information extracted from word embeddings","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-frame-instance-relatedness","title":"Measuring Frame Instance Relatedness","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ntu-nlp-lab-system-at-semeval-2018-task-10","title":"NTU NLP Lab System at SemEval-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"peperomia-at-semeval-2018-task-2-vector","title":"Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"phrase-level-metaphor-identification-using","title":"Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-human-metaphor-paraphrase","title":"Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2018-task-10-capturing-discriminative","title":"SemEval-2018 Task 10: Capturing Discriminative Attributes","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-measures-for-the-detection-of","title":"Similarity Measures for the Detection of Clinical Conditions with Verbal Fluency Tasks","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-word-analogy-testing-caveat","title":"The Word Analogy Testing Caveat","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"thu_ngn-at-semeval-2018-task-10-capturing","title":"THU\\_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"umd-at-semeval-2018-task-10-can-word","title":"UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unbnlp-at-semeval-2018-task-10-evaluating","title":"UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unimelb-at-semeval-2018-task-12-generative","title":"UniMelb at SemEval-2018 Task 12: Generative Implication using LSTMs, Siamese Networks and Semantic Representations with Synonym Fuzzing","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wolves-at-semeval-2018-task-10-semantic","title":"Wolves at SemEval-2018 Task 10: Semantic Discrimination based on Knowledge and Association","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-label-transfer-learning-for-semantic","title":"Multi-Label Transfer Learning for Multi-Relational Semantic Similarity","date":"2018-05-31","arxiv_id":"1805.12501","repositories_listed":0,"syntology":null},{"url":null,"slug":"legal-document-retrieval-using-document","title":"Legal Document Retrieval using Document Vector Embeddings and Deep Learning","date":"2018-05-27","arxiv_id":"1805.10685","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-modeling-via-multiple-word","title":"Sentence Modeling via Multiple Word Embeddings and Multi-level Comparison for Semantic Textual Similarity","date":"2018-05-21","arxiv_id":"1805.07882","repositories_listed":0,"syntology":null},{"url":null,"slug":"weight-initialization-in-neural-language","title":"Weight Initialization in Neural Language Models","date":"2018-05-12","arxiv_id":"1805.06503","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-rank-based-similarity-metric-for-word","title":"A Rank-Based Similarity Metric for Word Embeddings","date":"2018-05-04","arxiv_id":"1805.01923","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-large-resource-of-patterns-for-verbal","title":"A Large Resource of Patterns for Verbal Paraphrases","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multilingual-dataset-for-evaluating","title":"A Multilingual Dataset for Evaluating Parallel Sentence Extraction from Comparable Corpora","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multilingual-wikified-data-set-of","title":"A Multilingual Wikified Data Set of Educational Material","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-automatically-constructed","title":"A Survey on Automatically-Constructed WordNets and their Evaluation: Lexical and Word Embedding-based Approaches","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"acquiring-verb-classes-through-bottom-up","title":"Acquiring Verb Classes Through Bottom-Up Semantic Verb Clustering","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-citation-distance-networks-for","title":"Analyzing Citation-Distance Networks for Evaluating Publication Impact","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-thesaurus-construction-for-modern","title":"Automatic Thesaurus Construction for Modern Hebrew","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualized-usage-based-material-selection","title":"Contextualized Usage-Based Material Selection","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-semantic-textual-similarity-for","title":"Fine-grained Semantic Textual Similarity for Serbian","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"frnewslink-a-corpus-linking-tv-broadcast-news","title":"FrNewsLink : a corpus linking TV Broadcast News Segments and Press Articles","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kit-multi-a-translation-oriented-multilingual","title":"KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowing-the-author-by-the-company-his-words","title":"Knowing the Author by the Company His Words Keep","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lexical-and-semantic-features-for-cross","title":"Lexical and Semantic Features for Cross-lingual Text Reuse Classification: an Experiment in English and Latin Paraphrases","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"metaphor-suggestions-based-on-a-semantic","title":"Metaphor Suggestions based on a Semantic Metaphor Repository","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"retrofitting-word-representations-for","title":"Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semr-11-a-multi-lingual-gold-standard-for","title":"SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"social-image-tags-as-a-source-of-word","title":"Social Image Tags as a Source of Word Embeddings: A Task-oriented Evaluation","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-gold-standard-corpus-for-variable","title":"Towards a Gold Standard Corpus for Variable Detection and Linking in Social Science Publications","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"direct-network-transfer-transfer-learning-of","title":"Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity","date":"2018-04-20","arxiv_id":"1804.07835","repositories_listed":0,"syntology":null},{"url":null,"slug":"similarity-between-learning-outcomes-from","title":"Similarity between Learning Outcomes from Course Objectives using Semantic Analysis, Blooms taxonomy and Corpus statistics","date":"2018-04-17","arxiv_id":"1804.06333","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-two-vietnamese-datasets-for","title":"Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness","date":"2018-04-15","arxiv_id":"1804.05388","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-a-ranking-function-for-open-domain","title":"Training a Ranking Function for Open-Domain Question Answering","date":"2018-04-12","arxiv_id":"1804.04264","repositories_listed":0,"syntology":null},{"url":null,"slug":"viewpoint-aware-video-summarization","title":"Viewpoint-aware Video Summarization","date":"2018-04-09","arxiv_id":"1804.02843","repositories_listed":0,"syntology":null},{"url":null,"slug":"dock-detecting-objects-by-transferring-common","title":"DOCK: Detecting Objects by transferring Common-sense Knowledge","date":"2018-04-03","arxiv_id":"1804.01077","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-word-embeddings-into-open","title":"Incorporating Word Embeddings into Open Directory Project based Large-scale Classification","date":"2018-04-03","arxiv_id":"1804.00828","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-architecture-for-credibility","title":"Neural Network Architecture for Credibility Assessment of Textual Claims","date":"2018-03-28","arxiv_id":"1803.10547","repositories_listed":0,"syntology":null},{"url":null,"slug":"equation-embeddings","title":"Equation Embeddings","date":"2018-03-24","arxiv_id":"1803.09123","repositories_listed":0,"syntology":null},{"url":null,"slug":"russe-the-first-workshop-on-russian-semantic","title":"RUSSE: The First Workshop on Russian Semantic Similarity","date":"2018-03-15","arxiv_id":"1803.05820","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-word-representations-for-bridging","title":"Enhanced Word Representations for Bridging Anaphora Resolution","date":"2018-03-13","arxiv_id":"1803.04790","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-context-exploring-semantic-similarity","title":"Beyond Context: Exploring Semantic Similarity for Tiny Face Detection","date":"2018-03-05","arxiv_id":"1803.01555","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attention-based-word-level-interaction","title":"An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering","date":"2018-01-30","arxiv_id":"1801.09893","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-paragram-and-glove-results-for","title":"Comparison of Paragram and GloVe Results for Similarity Benchmarks","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-language-impairments-in-autism-a","title":"Detecting Language Impairments in Autism: A Computational Analysis of Semi-structured Conversations with Vector Semantics","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-the-stability-of-embedding-based","title":"Evaluating the Stability of Embedding-based Word Similarities","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sound-analogies-with-phoneme-embeddings","title":"Sound Analogies with Phoneme Embeddings","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"soft-seeded-ssl-graphs-for-unsupervised","title":"Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval","date":"2017-12-15","arxiv_id":"1712.05574","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-modality-invariant-representations","title":"Learning Modality-Invariant Representations for Speech and Images","date":"2017-12-11","arxiv_id":"1712.03897","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-networks-for-semantic-textual","title":"Neural Networks for Semantic Textual Similarity","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semtagger-a-novel-approach-for-semantic","title":"SemTagger: A Novel Approach for Semantic Similarity Based Hashtag Recommendation on Twitter","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"suggesting-sentences-for-esl-using-kernel","title":"Suggesting Sentences for ESL using Kernel Embeddings","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"calculating-semantic-similarity-between","title":"Calculating Semantic Similarity between Academic Articles using Topic Event and Ontology","date":"2017-11-30","arxiv_id":"1711.11508","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-cross-lingual-word-sense","title":"Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian","date":"2017-11-16","arxiv_id":"1711.06196","repositories_listed":0,"syntology":null},{"url":"/paper/paranmt-50m-pushing-the-limits-of","slug":"paranmt-50m-pushing-the-limits-of","title":"ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations","date":"2017-11-15","arxiv_id":"1711.05732","repositories_listed":0,"syntology":null},{"url":null,"slug":"are-manually-prepared-affective-lexicons","title":"Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspherical-query-likelihood-models-with","title":"Hyperspherical Query Likelihood Models with Word Embeddings","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-semantic-relations-between-human","title":"Measuring Semantic Relations between Human Activities","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-features-based-on-word-alignments","title":"Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-similarity-analysis-for-paraphrase","title":"Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ssas-semantic-similarity-for-abstractive","title":"SSAS: Semantic Similarity for Abstractive Summarization","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-challenge-of-composition-in","title":"The Challenge of Composition in Distributional and Formal Semantics","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"turning-distributional-thesauri-into-word","title":"Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-the-limits-of-unsupervised-learning","title":"Testing the limits of unsupervised learning for semantic similarity","date":"2017-10-23","arxiv_id":"1710.08246","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semantically-motivated-approach-to-compute","title":"A Semantically Motivated Approach to Compute ROUGE Scores","date":"2017-10-20","arxiv_id":"1710.07441","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-sentence-representations-as-word","title":"Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF","date":"2017-10-17","arxiv_id":"1710.06524","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-for-structured","title":"Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer","date":"2017-10-16","arxiv_id":"1710.05918","repositories_listed":0,"syntology":null},{"url":null,"slug":"analise-de-medidas-de-similaridade-semantica","title":"An\\'alise de Medidas de Similaridade Sem\\^antica na Tarefa de Reconhecimento de Implica\\cc\\~ao Textual (Analysis of Semantic Similarity Measures in the Recognition of Textual Entailment Task)[In Portuguese]","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"avaliando-a-similaridade-semantica-entre","title":"Avaliando a similaridade sem\\^antica entre frases curtas atrav\\'es de uma abordagem h\\'\\ibrida (A hybrid approach to measure Semantic Textual Similarity between short sentences in Brazilian Portuguese)[In Portuguese]","date":"2017-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bag-of-vector-embeddings-of-dependency-graphs","title":"Bag-of-Vector Embeddings of Dependency Graphs for Semantic Induction","date":"2017-09-30","arxiv_id":"1710.00205","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-enhanced-method-to-compute-the-similarity","title":"An enhanced method to compute the similarity between concepts of ontology","date":"2017-09-26","arxiv_id":"1709.08880","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrofitting-concept-vector-representations","title":"Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness","date":"2017-09-21","arxiv_id":"1709.07357","repositories_listed":0,"syntology":null},{"url":null,"slug":"doctoral-advisor-or-medical-condition-towards","title":"Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]","date":"2017-09-20","arxiv_id":"1709.06907","repositories_listed":0,"syntology":null},{"url":null,"slug":"methodology-and-results-for-the-competition","title":"Methodology and Results for the Competition on Semantic Similarity Evaluation and Entailment Recognition for PROPOR 2016","date":"2017-09-19","arxiv_id":"1709.08694","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-summarization-to-discover-argument-1","title":"Using Summarization to Discover Argument Facets in Online Ideological Dialog","date":"2017-09-03","arxiv_id":"1709.00662","repositories_listed":0,"syntology":null},{"url":null,"slug":"author-aware-aspect-topic-sentiment-model-to","title":"Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-frames-at-the-sentence-level-in","title":"Classifying Frames at the Sentence Level in News Articles","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"czech-dataset-for-semantic-similarity-and","title":"Czech Dataset for Semantic Similarity and Relatedness","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-stylistic-variations-in","title":"Discovering Stylistic Variations in Distributional Vector Space Models via Lexical Paraphrases","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distractor-generation-for-chinese-fill-in-the","title":"Distractor Generation for Chinese Fill-in-the-blank Items","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-tensor-model-for-detecting-asymmetric","title":"Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-vector-spaces-for-semantic","title":"Exploring Vector Spaces for Semantic Relations","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inter-weighted-alignment-network-for-sentence","title":"Inter-Weighted Alignment Network for Sentence Pair Modeling","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-space-embedding-for-retrieval-in","title":"Latent Space Embedding for Retrieval in Question-Answer Archives","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-score-system-summaries-for-better","title":"Learning to Score System Summaries for Better Content Selection Evaluation.","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lexical-chains-meet-word-embeddings-in","title":"Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"monolingual-phrase-alignment-on-parse-forests","title":"Monolingual Phrase Alignment on Parse Forests","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parameter-transfer-across-domains-for-word","title":"Parameter Transfer across Domains for Word Sense Disambiguation","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"84bb99efb3a564562a959fd6f1c7f3777e50eb749d819e180d67e5f854f7f3a4","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}