{"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/18","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":18,"pages_in_order":24,"rows_per_page":100,"rows":[1701,1800],"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/17","next":"/task/semantic-textual-similarity/papers/19","papers":[{"url":null,"slug":"sentence-level-multilingual-multi-modal","title":"Sentence-Level Multilingual Multi-modal Embedding for Natural Language Processing","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"speaking-seeing-understanding-correlating","title":"Speaking, Seeing, Understanding: Correlating semantic models with conceptual representation in the brain","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-effect-of-negative-sampling-strategy-on","title":"The Effect of Negative Sampling Strategy on Capturing Semantic Similarity in Document Embeddings","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-strange-geometry-of-skip-gram-with","title":"The strange geometry of skip-gram with negative sampling","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-inference-for-logical-inference","title":"Variational Inference for Logical Inference","date":"2017-09-01","arxiv_id":"1709.00224","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embeddings-based-on-fixed-size-ordinally","title":"Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clac-semantic-relatedness-of-words-and","title":"ClaC: Semantic Relatedness of Words and Phrases","date":"2017-08-19","arxiv_id":"1708.05801","repositories_listed":0,"syntology":null},{"url":null,"slug":"gold-standard-online-debates-summaries-and","title":"Gold Standard Online Debates Summaries and First Experiments Towards Automatic Summarization of Online Debate Data","date":"2017-08-15","arxiv_id":"1708.04592","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-graph-based-semi-supervised-approach-for","title":"A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bit-at-semeval-2017-task-1-using-semantic","title":"BIT at SemEval-2017 Task 1: Using Semantic Information Space to Evaluate Semantic Textual Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bucc-2017-shared-task-a-first-attempt-toward","title":"BUCC 2017 Shared Task: a First Attempt Toward a Deep Learning Framework for Identifying Parallel Sentences in Comparable Corpora","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-approaches-for-automatic-question","title":"Comparing Approaches for Automatic Question Identification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"does-free-word-order-hurt-assessing-the","title":"Does Free Word Order Hurt? Assessing the Practical Lexical Function Model for Croatian","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dt_team-at-semeval-2017-task-1-semantic","title":"DT\\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ecnu-at-semeval-2017-task-1-leverage-kernel","title":"ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ecnu-at-semeval-2017-task-3-using-traditional","title":"ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ecnu-at-semeval-2017-task-7-using-supervised","title":"ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-text-coherence-based-on-semantic","title":"Evaluating text coherence based on semantic similarity graph","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fcicu-at-semeval-2017-task-1-sense-based","title":"FCICU at SemEval-2017 Task 1: Sense-Based Language Independent Semantic Textual Similarity Approach","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hccl-at-semeval-2017-task-2-combining","title":"HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hcti-at-semeval-2017-task-1-use-convolutional","title":"HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hhu-at-semeval-2017-task-2-fast-hash-based","title":"HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"information-navigation-system-with","title":"Information Navigation System with Discovering User Interests","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"itnlp-aikf-at-semeval-2017-task-1-rich","title":"ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"l2finesc-id-at-semeval-2017-tasks-1-and-2","title":"L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-antonyms-with-paraphrases-and-a","title":"Learning Antonyms with Paraphrases and a Morphology-Aware Neural Network","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-contextual-embeddings-for-structural","title":"Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lim-lig-at-semeval-2017-task1-enhancing-the","title":"LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lipn-iimas-at-semeval-2017-task-1-subword","title":"LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lsis-at-semeval-2017-task-4-using-adapted","title":"LSIS at SemEval-2017 Task 4: Using Adapted Sentiment Similarity Seed Words For English and Arabic Tweet Polarity Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lump-at-semeval-2017-task-1-towards-an","title":"Lump at SemEval-2017 Task 1: Towards an Interlingua Semantic Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mahtab-at-semeval-2017-task-2-combination-of","title":"Mahtab at SemEval-2017 Task 2: Combination of Corpus-based and Knowledge-based Methods to Measure Semantic Word Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mitre-at-semeval-2017-task-1-simple-semantic","title":"MITRE at SemEval-2017 Task 1: Simple Semantic Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"opi-jsa-at-semeval-2017-task-1-application-of","title":"OPI-JSA at SemEval-2017 Task 1: Application of Ensemble learning for computing semantic textual similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"purduenlp-at-semeval-2017-task-1-predicting","title":"PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qlut-at-semeval-2017-task-1-semantic-textual","title":"QLUT at SemEval-2017 Task 1: Semantic Textual Similarity Based on Word Embeddings","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qu-bigir-at-semeval-2017-task-3-using","title":"QU-BIGIR at SemEval 2017 Task 3: Using Similarity Features for Arabic Community Question Answering Forums","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ressim-at-semeval-2017-task-1-multilingual","title":"ResSim at SemEval-2017 Task 1: Multilingual Word Representations for Semantic Textual Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rtm-at-semeval-2017-task-1-referential","title":"RTM at SemEval-2017 Task 1: Referential Translation Machines for Predicting Semantic Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rufino-at-semeval-2017-task-2-cross-lingual","title":"RUFINO at SemEval-2017 Task 2: Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh's-like list","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sefuhh-at-semeval-2017-task-1-unsupervised","title":"SEF@UHH at SemEval-2017 Task 1: Unsupervised Knowledge-Free Semantic Textual Similarity via Paragraph Vector","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2017-task-1-semantic-textual-1","title":"SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2017-task-2-multilingual-and-cross","title":"SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simbow-at-semeval-2017-task-3-soft-cosine","title":"SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sts-uhh-at-semeval-2017-task-1-scoring","title":"STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"takelab-qa-at-semeval-2017-task-3","title":"TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"talla-at-semeval-2017-task-3-identifying","title":"Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"target-word-prediction-and-paraphasia","title":"Target word prediction and paraphasia classification in spoken discourse","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uinsuska-titech-at-semeval-2017-task-3","title":"UINSUSKA-TiTech at SemEval-2017 Task 3: Exploiting Word Importance Levels for Similarity Features for CQA","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"umdeep-at-semeval-2017-task-1-end-to-end","title":"UMDeep at SemEval-2017 Task 1: End-to-End Shared Weight LSTM Model for Semantic Textual Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ways-of-asking-and-replying-in-duplicate","title":"Ways of Asking and Replying in Duplicate Question Detection","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-analogies-reveal-about-word-vectors-and","title":"What Analogies Reveal about Word Vectors and their Compositionality","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wild-devs-at-semeval-2017-task-2-using-neural","title":"Wild Devs' at SemEval-2017 Task 2: Using Neural Networks to Discover Word Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-pivoting-for-learning-multilingual","title":"Image Pivoting for Learning Multilingual Multimodal Representations","date":"2017-07-24","arxiv_id":"1707.07601","repositories_listed":0,"syntology":null},{"url":null,"slug":"visually-aligned-word-embeddings-for","title":"Visually Aligned Word Embeddings for Improving Zero-shot Learning","date":"2017-07-18","arxiv_id":"1707.05427","repositories_listed":0,"syntology":null},{"url":"/paper/biosses-a-semantic-sentence-similarity","slug":"biosses-a-semantic-sentence-similarity","title":"BIOSSES: A Semantic Sentence Similarity Estimation System for the Biomedical Domain","date":"2017-07-15","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"apples-to-apples-learning-semantics-of-common","title":"Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-instance-level-image-retrieval","title":"Beyond Instance-Level Image Retrieval: Leveraging Captions to Learn a Global Visual Representation for Semantic Retrieval","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evinets-neural-networks-for-combining","title":"EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-semantic-preserving-hashing-for-n","title":"Generalized Semantic Preserving Hashing for N-Label Cross-Modal Retrieval","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"online-asymmetric-similarity-learning-for","title":"Online Asymmetric Similarity Learning for Cross-Modal Retrieval","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-word-clusters-using-signed-spectral","title":"Semantic Word Clusters Using Signed Spectral Clustering","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"synergistic-union-of-word2vec-and-lexicon-for","title":"Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity","date":"2017-06-06","arxiv_id":"1706.01967","repositories_listed":0,"syntology":null},{"url":null,"slug":"am-elioration-de-la-similarit-e-s-emantique","title":"Am\\'elioration de la similarit\\'e s\\'emantique vectorielle par m\\'ethodes non-supervis\\'ees (Improved the Semantic Similarity with Weighting Vectors)","date":"2017-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cr-eation-et-validation-de-signatures-s","title":"Cr\\'eation et validation de signatures s\\'emantiques : application \\`a la mesure de similarit\\'e s\\'emantique et \\`a la substitution lexicale (Creating and validating semantic signatures : application for measuring semantic similarity and lexical substitution)","date":"2017-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simbow-une-mesure-de-similarit-e-s-emantique","title":"Simbow : une mesure de similarit\\'e s\\'emantique entre textes (Simbow : a semantic similarity metric between texts)","date":"2017-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-learning-with-gans-manifold","title":"Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference","date":"2017-05-24","arxiv_id":"1705.08850","repositories_listed":0,"syntology":null},{"url":null,"slug":"relevance-based-word-embedding","title":"Relevance-based Word Embedding","date":"2017-05-09","arxiv_id":"1705.03556","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-learning-of-semantic-textual","title":"Cross-lingual Learning of Semantic Textual Similarity with Multilingual Word Representations","date":"2017-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-vectors-reuse-and-replicability-towards","title":"Word vectors, reuse, and replicability: Towards a community repository of large-text resources","date":"2017-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"representing-sentences-as-low-rank-subspaces","title":"Representing Sentences as Low-Rank Subspaces","date":"2017-04-18","arxiv_id":"1704.05358","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-similarity-from-natural-language-and","title":"Semantic Similarity from Natural Language and Ontology Analysis","date":"2017-04-18","arxiv_id":"1704.05295","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-media-similarity-metric-learning-with","title":"Cross-media Similarity Metric Learning with Unified Deep Networks","date":"2017-04-14","arxiv_id":"1704.04333","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-word-embeddings-for-unsupervised","title":"Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization","date":"2017-04-10","arxiv_id":"1704.02963","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-multi-sense-embeddings-for-german","title":"Applying Multi-Sense Embeddings for German Verbs to Determine Semantic Relatedness and to Detect Non-Literal Language","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bilingual-lexicon-induction-by-learning-to","title":"Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bootstrapping-unsupervised-bilingual-lexicon","title":"Bootstrapping Unsupervised Bilingual Lexicon Induction","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"building-lexical-vector-representations-from","title":"Building Lexical Vector Representations from Concept Definitions","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"complex-verbs-are-different-exploring-the","title":"Complex Verbs are Different: Exploring the Visual Modality in Multi-Modal Models to Predict Compositionality","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-syntactically-informed","title":"Cross-Lingual Syntactically Informed Distributed Word Representations","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-document-and-phrase-co-embeddings","title":"Distributed Document and Phrase Co-embeddings for Descriptive Clustering","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-by-association-a-systematic-study","title":"Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iit-bhu-system-description-for-lsdsem17","title":"IIT (BHU): System Description for LSDSem'17 Shared Task","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-rouge-for-timeline-summarization","title":"Improving ROUGE for Timeline Summarization","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-verb-metaphor-detection-by","title":"Improving Verb Metaphor Detection by Propagating Abstractness to Words, Phrases and Individual Senses","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"is-this-a-child-a-girl-or-a-car-exploring-the","title":"Is this a Child, a Girl or a Car? Exploring the Contribution of Distributional Similarity to Learning Referential Word Meanings","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-evaluation-of-dependency-based","title":"Large-scale evaluation of dependency-based DSMs: Are they worth the effort?","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"literal-or-idiomatic-identifying-the-reading","title":"Literal or idiomatic? Identifying the reading of single occurrences of German multiword expressions using word embeddings","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-similarity-of-arabic-sentences-with","title":"Semantic Similarity of Arabic Sentences with Word Embeddings","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-idiomatic-variation","title":"Understanding Idiomatic Variation","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-sense-filtering-improves-embedding-based","title":"Word Sense Filtering Improves Embedding-Based Lexical Substitution","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-similarity-datasets-for-indian-languages","title":"Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-vector-space-specialisation","title":"Word Vector Space Specialisation","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bundle-optimization-for-multi-aspect","title":"Bundle Optimization for Multi-aspect Embedding","date":"2017-03-29","arxiv_id":"1703.09928","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-deep-metric-learning-with-multi","title":"Cross-modal Deep Metric Learning with Multi-task Regularization","date":"2017-03-21","arxiv_id":"1703.07026","repositories_listed":0,"syntology":null},{"url":null,"slug":"neobility-at-semeval-2017-task-1-an-attention","title":"Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model","date":"2017-03-16","arxiv_id":"1703.05465","repositories_listed":0,"syntology":null},{"url":null,"slug":"story-cloze-ending-selection-baselines-and","title":"Story Cloze Ending Selection Baselines and Data Examination","date":"2017-03-13","arxiv_id":"1703.04330","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-metrics-of-distance-and","title":"A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection","date":"2017-03-10","arxiv_id":"1703.03640","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-comprehensive-approach-for-estimating","title":"A Novel Comprehensive Approach for Estimating Concept Semantic Similarity in WordNet","date":"2017-03-06","arxiv_id":"1703.01726","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-knowledge-based-approach-to-word-sense","title":"A Knowledge-Based Approach to Word Sense Disambiguation by distributional selection and semantic features","date":"2017-02-27","arxiv_id":"1702.08450","repositories_listed":0,"syntology":null}],"record_sha256":"46fc2fbad6b49c17c71bbefb1d0055b5bc0e49f8042daea73aca00a30cc86200","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}