{"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":"/method/glove/papers/4","list_of":"/method/glove","method":"GloVe","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":4,"pages_in_order":4,"rows_per_page":100,"rows":[301,357],"of":357,"counts":{"archive_papers_tagged":357,"with_a_code_link":116,"where_syntology_ran_a_sample":10,"not_listed_spam_title":0,"listed":357,"listed_where_code_ran":10,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":8,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":8,"listed_every_run_a_failure_of_syntologys_instrument":2,"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":"/method/glove","prev":"/method/glove/papers/3","next":null,"papers":[{"paper":"/paper/glomo-unsupervisedly-learned-relational","slug":"glomo-unsupervisedly-learned-relational","title":"GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations","date":"2018-06-14","arxiv_id":"1806.05662","n_code_links":1,"syntology":null},{"paper":"/paper/interactive-classification-for-deep-learning","slug":"interactive-classification-for-deep-learning","title":"Interactive Classification for Deep Learning Interpretation","date":"2018-06-14","arxiv_id":"1806.05660","n_code_links":1,"syntology":null},{"paper":null,"slug":"fmri-semantic-category-decoding-using","title":"fMRI Semantic Category Decoding using Linguistic Encoding of Word Embeddings","date":"2018-06-13","arxiv_id":"1806.05177","n_code_links":0,"syntology":null},{"paper":null,"slug":"absolute-orientation-for-word-embedding","title":"Closed Form Word Embedding Alignment","date":"2018-06-04","arxiv_id":"1806.01330","n_code_links":0,"syntology":null},{"paper":null,"slug":"amritanlp-at-semeval-2018-task-10-capturing","title":"AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"attr2vec-jointly-learning-word-and-contextual","title":"attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ironymagnet-at-semeval-2018-task-3-a-siamese","title":"IronyMagnet at SemEval-2018 Task 3: A Siamese network for Irony detection in Social media","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"jiangnan-at-semeval-2018-task-11-deep-neural","title":"Jiangnan at SemEval-2018 Task 11: Deep Neural Network with Attention Method for Machine Comprehension Task","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"teamcen-at-semeval-2018-task-1-global-vectors","title":"TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"teamdl-at-semeval-2018-task-8-cybersecurity","title":"TeamDL at SemEval-2018 Task 8: Cybersecurity Text Analysis using Convolutional Neural Network and Conditional Random Fields","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynu_deep-at-semeval-2018-task-11-an-ensemble","title":"YNU\\_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"what-the-vec-towards-probabilistically","title":"What the Vec? Towards Probabilistically Grounded Embeddings","date":"2018-05-30","arxiv_id":"1805.12164","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-statistical-and-semantic-models-for","title":"Using Statistical and Semantic Models for Multi-Document Summarization","date":"2018-05-11","arxiv_id":"1805.04579","n_code_links":0,"syntology":null},{"paper":"/paper/extrofitting-enriching-word-representation","slug":"extrofitting-enriching-word-representation","title":"Extrofitting: Enriching Word Representation and its Vector Space with Semantic Lexicons","date":"2018-04-21","arxiv_id":"1804.07946","n_code_links":2,"syntology":null},{"paper":"/paper/mittens-an-extension-of-glove-for-learning","slug":"mittens-an-extension-of-glove-for-learning","title":"Mittens: An Extension of GloVe for Learning Domain-Specialized Representations","date":"2018-03-27","arxiv_id":"1803.09901","n_code_links":1,"syntology":null},{"paper":"/paper/a-compressed-sensing-view-of-unsupervised","slug":"a-compressed-sensing-view-of-unsupervised","title":"A Compressed Sensing View of Unsupervised Text Embeddings, Bag-of-n-Grams, and LSTMs","date":"2018-01-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":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,"n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-building-affect-sensitive-word","title":"Towards Building Affect sensitive Word Distributions","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"word-representation-or-word-embedding-in","title":"word representation or word embedding in Persian text","date":"2017-12-18","arxiv_id":"1712.06674","n_code_links":0,"syntology":null},{"paper":null,"slug":"iiit-h-at-ijcnlp-2017-task-4-customer","title":"IIIT-H at IJCNLP-2017 Task 4: Customer Feedback Analysis using Machine Learning and Neural Network Approaches","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ynu-hpcc-at-ijcnlp-2017-task-4-attention","title":"YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/improving-the-accuracy-of-pre-trained-word","slug":"improving-the-accuracy-of-pre-trained-word","title":"Improving the Accuracy of Pre-trained Word Embeddings for Sentiment Analysis","date":"2017-11-23","arxiv_id":"1711.08609","n_code_links":1,"syntology":null},{"paper":"/paper/spine-sparse-interpretable-neural-embeddings","slug":"spine-sparse-interpretable-neural-embeddings","title":"SPINE: SParse Interpretable Neural Embeddings","date":"2017-11-23","arxiv_id":"1711.08792","n_code_links":2,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["harsh19/SPINE"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"modeling-semantic-relatedness-using-global","title":"Modeling Semantic Relatedness using Global Relation Vectors","date":"2017-11-14","arxiv_id":"1711.05294","n_code_links":0,"syntology":null},{"paper":null,"slug":"multilingual-vector-representations-of-words","title":"Multilingual Vector Representations of Words, Sentences, and Documents","date":"2017-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/local-word-vectors-guiding-keyphrase","slug":"local-word-vectors-guiding-keyphrase","title":"Local Word Vectors Guiding Keyphrase Extraction","date":"2017-10-20","arxiv_id":"1710.07503","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-domain-specific-word-embeddings-from","title":"Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts","date":"2017-09-21","arxiv_id":"1709.07470","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-word-embeddings-against","title":"Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks","date":"2017-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"joint-unsupervised-learning-of-semantic","title":"Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees","date":"2017-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ngram2vec-learning-improved-word","title":"Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics","date":"2017-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-word-association-strengths","title":"Predicting Word Association Strengths","date":"2017-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/rotated-word-vector-representations-and-their","slug":"rotated-word-vector-representations-and-their","title":"Rotated Word Vector Representations and their Interpretability","date":"2017-09-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/tips-and-tricks-for-visual-question-answering","slug":"tips-and-tricks-for-visual-question-answering","title":"Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge","date":"2017-08-09","arxiv_id":"1708.02711","n_code_links":10,"syntology":null},{"paper":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,"n_code_links":0,"syntology":null},{"paper":null,"slug":"humorhawk-at-semeval-2017-task-6-mixing","title":"HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/learned-in-translation-contextualized-word","slug":"learned-in-translation-contextualized-word","title":"Learned in Translation: Contextualized Word Vectors","date":"2017-08-01","arxiv_id":"1708.00107","n_code_links":5,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["salesforce/cove"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/crnn-a-joint-neural-network-for-redundancy","slug":"crnn-a-joint-neural-network-for-redundancy","title":"CRNN: A Joint Neural Network for Redundancy Detection","date":"2017-06-04","arxiv_id":"1706.01069","n_code_links":1,"syntology":null},{"paper":null,"slug":"relevance-based-word-embedding","title":"Relevance-based Word Embedding","date":"2017-05-09","arxiv_id":"1705.03556","n_code_links":0,"syntology":null},{"paper":null,"slug":"eve-explainable-vector-based-embedding","title":"EVE: Explainable Vector Based Embedding Technique Using Wikipedia","date":"2017-02-22","arxiv_id":"1702.06891","n_code_links":0,"syntology":null},{"paper":"/paper/all-but-the-top-simple-and-effective","slug":"all-but-the-top-simple-and-effective","title":"All-but-the-Top: Simple and Effective Postprocessing for Word Representations","date":"2017-02-05","arxiv_id":"1702.01417","n_code_links":4,"syntology":{"ran":21,"of":26,"n_ran_checked":21,"n_instrument":0,"unverified":5,"pointer_only":2,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 0 honoured, 0 violated, 21 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":null,"slug":"d-glove-a-feasible-least-squares-model-for","title":"D-GloVe: A Feasible Least Squares Model for Estimating Word Embedding Densities","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"personality-estimation-from-japanese-text","title":"Personality Estimation from Japanese Text","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"word-and-document-embeddings-based-on-neural","title":"Word and Document Embeddings based on Neural Network Approaches","date":"2016-11-18","arxiv_id":"1611.05962","n_code_links":0,"syntology":null},{"paper":"/paper/quasi-recurrent-neural-networks","slug":"quasi-recurrent-neural-networks","title":"Quasi-Recurrent Neural Networks","date":"2016-11-05","arxiv_id":"1611.01576","n_code_links":7,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/language-models-with-pre-trained-glove-word","slug":"language-models-with-pre-trained-glove-word","title":"Language Models with Pre-Trained (GloVe) Word Embeddings","date":"2016-10-12","arxiv_id":"1610.03759","n_code_links":1,"syntology":null},{"paper":null,"slug":"hash2vec-feature-hashing-for-word-embeddings","title":"Hash2Vec, Feature Hashing for Word Embeddings","date":"2016-08-31","arxiv_id":"1608.08940","n_code_links":0,"syntology":null},{"paper":"/paper/semantics-derived-automatically-from-language","slug":"semantics-derived-automatically-from-language","title":"Semantics derived automatically from language corpora contain human-like biases","date":"2016-08-25","arxiv_id":"1608.07187","n_code_links":1,"syntology":null},{"paper":"/paper/new-word-analogy-corpus-for-exploring","slug":"new-word-analogy-corpus-for-exploring","title":"New word analogy corpus for exploring embeddings of Czech words","date":"2016-08-02","arxiv_id":"1608.00789","n_code_links":1,"syntology":null},{"paper":null,"slug":"query-expansion-with-locally-trained-word","title":"Query Expansion with Locally-Trained Word Embeddings","date":"2016-05-25","arxiv_id":"1605.07891","n_code_links":0,"syntology":null},{"paper":null,"slug":"spanish-word-vectors-from-wikipedia","title":"Spanish Word Vectors from Wikipedia","date":"2016-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/the-hunvec-framework-for-nn-crf-based","slug":"the-hunvec-framework-for-nn-crf-based","title":"The hunvec framework for NN-CRF-based sequential tagging","date":"2016-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/an-ensemble-method-to-produce-high-quality","slug":"an-ensemble-method-to-produce-high-quality","title":"An Ensemble Method to Produce High-Quality Word Embeddings (2016)","date":"2016-04-06","arxiv_id":"1604.01692","n_code_links":1,"syntology":null},{"paper":"/paper/multi-layer-representation-learning-for","slug":"multi-layer-representation-learning-for","title":"Multi-layer Representation Learning for Medical Concepts","date":"2016-02-17","arxiv_id":"1602.05568","n_code_links":2,"syntology":null},{"paper":"/paper/rand-walk-a-latent-variable-model-approach-to","slug":"rand-walk-a-latent-variable-model-approach-to","title":"A Latent Variable Model Approach to PMI-based Word Embeddings","date":"2015-02-12","arxiv_id":"1502.03520","n_code_links":4,"syntology":null},{"paper":null,"slug":"navigating-the-semantic-horizon-using","title":"Navigating the Semantic Horizon using Relative Neighborhood Graphs","date":"2015-01-12","arxiv_id":"1501.02670","n_code_links":0,"syntology":null},{"paper":null,"slug":"linking-glove-with-word2vec","title":"Linking GloVe with word2vec","date":"2014-11-20","arxiv_id":"1411.5595","n_code_links":0,"syntology":null},{"paper":"/paper/glove-global-vectors-for-word-representation","slug":"glove-global-vectors-for-word-representation","title":"GloVe: Global Vectors for Word Representation","date":"2014-10-01","arxiv_id":null,"n_code_links":4,"syntology":null}],"record_sha256":"ae0df80dbe8f3e34363991a571fbbd08e004dfb806c80cb757bbea3825b2e289","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}