{"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/sts/papers/4","list_of":"/task/sts","task":"STS","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":4,"pages_in_order":4,"rows_per_page":100,"rows":[301,334],"of":334,"counts":{"archive_papers_tagged":334,"with_a_code_link":129,"where_syntology_ran_a_sample":28,"not_listed_spam_title":0,"listed":334,"listed_where_code_ran":28,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":25,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":25,"listed_every_run_a_failure_of_syntologys_instrument":3,"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/sts","prev":"/task/sts/papers/3","next":null,"papers":[{"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"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":"ph-t-duality-wall-properties-and-time","title":"pH/$T$ duality - wall properties and time evolution of plant cells","date":"2017-07-27","arxiv_id":"1505.00327","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":"multilingual-multi-modal-embeddings-for","title":"Multilingual Multi-modal Embeddings for Natural Language Processing","date":"2017-02-03","arxiv_id":"1702.01101","repositories_listed":0,"syntology":null},{"url":"/paper/interpretable-semantic-textual-similarity","slug":"interpretable-semantic-textual-similarity","title":"Interpretable Semantic Textual Similarity: Finding and explaining differences between sentences","date":"2016-12-14","arxiv_id":"1612.04868","repositories_listed":0,"syntology":null},{"url":null,"slug":"task-oriented-intrinsic-evaluation-of","title":"Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gwu-nlp-at-semeval-2016-shared-task-1-matrix","title":"GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iiscnlp-at-semeval-2016-task-2-interpretable","title":"IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iubc-at-semeval-2016-task-2-rnns-and-lstms","title":"iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STS","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rtm-at-semeval-2016-task-1-predicting","title":"RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extending-monolingual-semantic-textual","title":"Extending Monolingual Semantic Textual Similarity Task to Multiple Cross-lingual Settings","date":"2016-05-01","arxiv_id":null,"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":null,"slug":"ubc-cubes-for-english-semantic-textual","title":"UBC: Cubes for English Semantic Textual Similarity and Supervised Approaches for Interpretable STS","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-short-image-series-based-scheme-for-time","title":"A Short Image Series Based Scheme for Time Series Digital Image Correlation","date":"2014-10-28","arxiv_id":"1410.7613","repositories_listed":0,"syntology":null},{"url":null,"slug":"important-molecular-descriptors-selection","title":"Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity","date":"2014-02-21","arxiv_id":"1402.5360","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-representation-of-action-sequences-how","title":"Neural representation of action sequences: how far can a simple snippet-matching model take us?","date":"2013-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sxucfn-core-sts-models-integrating-framenet","title":"SXUCFN-Core: STS Models Integrating FrameNet Parsing Information","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ucam-core-incorporating-structured","title":"UCAM-CORE: Incorporating structured distributional similarity into STS","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stanford-probabilistic-edit-distance-metrics","title":"Stanford: Probabilistic Edit Distance Metrics for STS","date":"2012-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tiantianzhu7system-description-of-semantic","title":"Tiantianzhu7:System Description of Semantic Textual Similarity (STS) in the SemEval-2012 (Task 6)","date":"2012-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dss-text-similarity-using-lexical-alignments","title":"DSS: Text Similarity Using Lexical Alignments of Form, Distributional Semantics and Grammatical Relations","date":"2012-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"0aaa34ea045b1de411015711e83f58064eb9a5a35762a0a83da020980af0733c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}