--- title: "Lexical semantics & word embeddings — papers" description: "Lexical semantics & word embeddings: 35 papers of the Elamur Research library, the most cited first." canonical: "https://research.elamur.ai/free/en/topics/lexical-semantics/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/topics/lexical-semantics/) [home](https://md.research.elamur.ai/free/en/) / [topics](https://md.research.elamur.ai/free/en/topics/) / Lexical semantics & word embeddings # 📖 Lexical semantics & word embeddings 35 papers · page 1 of 2 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/) The meaning of words: senses, relations, lexicons, and words as vectors. 💡 Explained: [Cognitive linguistics](https://md.research.elamur.ai/free/en/concepts/cognitive-linguistics/) · [Distributional semantics](https://md.research.elamur.ai/free/en/concepts/distributional-semantics/) · [Lexical semantics](https://md.research.elamur.ai/free/en/concepts/lexical-semantics/) · [Lexicography](https://md.research.elamur.ai/free/en/concepts/lexicography/) · [Semantic similarity](https://md.research.elamur.ai/free/en/concepts/semantic-similarity/) · [Word embeddings](https://md.research.elamur.ai/free/en/concepts/word-embeddings/) · [Word sense disambiguation](https://md.research.elamur.ai/free/en/concepts/word-sense-disambiguation/) · [Zipf's law](https://md.research.elamur.ai/free/en/concepts/zipfs-law/) **1** [2](https://md.research.elamur.ai/free/en/topics/lexical-semantics/page/2/) [next →](https://md.research.elamur.ai/free/en/topics/lexical-semantics/page/2/) - [Evaluating WordNet-based Measures of Lexical Semantic Relatedness](https://md.research.elamur.ai/free/en/papers/budanitsky-2006-evaluating-wordnet-based-measures-lexical/) — Alexander Budanitsky, Graeme Hirst · 2006 · 1 610 citations - [A Structured Vector Space Model for Word Meaning in Context](https://md.research.elamur.ai/free/en/papers/erk-2008-structured-vector-space-model-word/) — Katrin Erk, Sebastian Padó · 2008 · 508 citations - [Two/Too Simple Adaptations of Word2Vec for Syntax Problems](https://md.research.elamur.ai/free/en/papers/ling-2015-two-too-simple-adaptations-word2vec/) — Wang Ling, Chris Dyer, Alan W. Black, et al. · 2015 · 504 citations - [Multi-Prototype Vector-Space Models of Word Meaning](https://md.research.elamur.ai/free/en/papers/reisinger-2010-multi-prototype-vector-space-models/) — Joseph Reisinger, Raymond J. Mooney · 2010 · 489 citations - [GermaNet - a Lexical-Semantic Net for German](https://md.research.elamur.ai/free/en/papers/hamp-1997-germanet-lexical-semantic-net-german/) — Birgit Hamp, Helmut Feldweg · 1997 · 466 citations - [Automatic construction of a hypernym-labeled noun hierarchy from text](https://md.research.elamur.ai/free/en/papers/caraballo-1999-automatic-construction-hypernym-labeled-noun/) — Sharon A. Caraballo · 1999 · 441 citations - [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://md.research.elamur.ai/free/en/papers/schlechtweg-2020-semeval-2020-task-1-unsupervised/) — Dominik Schlechtweg, Barbara McGillivray, Simon Hengchen, et al. · 2020 · 351 citations - [SensEmbed: Learning Sense Embeddings for Word and Relational Similarity](https://md.research.elamur.ai/free/en/papers/iacobacci-2015-sensembed-learning-sense-embeddings-word/) — Ignacio Iacobacci, Mohammad Taher Pilehvar, Roberto Navigli · 2015 · 348 citations - [Probing Pretrained Language Models for Lexical Semantics](https://md.research.elamur.ai/free/en/papers/vulic-2020-probing-pretrained-language-models-lexical/) — Ivan Vulić, Edoardo Maria Ponti, Robert Litschko, et al. · 2020 · 320 citations - [Improving Hypernymy Detection with an Integrated Path-based and Distributional Method](https://md.research.elamur.ai/free/en/papers/shwartz-2016-improving-hypernymy-detection-integrated-path/) — Vered Shwartz, Yoav Goldberg, Ido Dagan · 2016 · 319 citations - [Near-Synonymy and Lexical Choice](https://md.research.elamur.ai/free/en/papers/edmonds-2002-near-synonymy-lexical-choice/) — Philip Edmonds, Graeme Hirst · 2002 · 300 citations - [The SALSA Corpus: a German Corpus Resource for Lexical Semantics](https://md.research.elamur.ai/free/en/papers/burchardt-2006-salsa-corpus-german-corpus-resource/) — Aljoscha Burchardt, Katrin Erk, Anette Frank, et al. · 2006 · 255 citations - [Linear Algebraic Structure of Word Senses, with Applications to Polysemy](https://md.research.elamur.ai/free/en/papers/arora-2018-linear-algebraic-structure-word-senses/) — Sanjeev Arora, Yuanzhi Li, Yingyu Liang, et al. · 2018 · 247 citations - [Learning to Distinguish Hypernyms and Co-Hyponyms](https://md.research.elamur.ai/free/en/papers/weeds-2014-learning-distinguish-hypernyms-co-hyponyms/) — Julie Weeds, Daoud Clarke, Jeremy Reffin, et al. · 2014 · 242 citations - [Analysing Lexical Semantic Change with Contextualised Word Representations](https://md.research.elamur.ai/free/en/papers/giulianelli-2020-analysing-lexical-semantic-change-contextualised/) — Mario Giulianelli, Marco Del Tredici, Raquel Fernández · 2020 · 239 citations - [SemEval-2007 Task 07: Coarse-Grained English All-Words Task](https://md.research.elamur.ai/free/en/papers/navigli-2007-semeval-2007-task-07-coarse/) — Roberto Navigli, Kenneth C. Litkowski, Orin Hargraves · 2007 · 226 citations - [Inclusive yet Selective: Supervised Distributional Hypernymy Detection](https://md.research.elamur.ai/free/en/papers/roller-2014-inclusive-yet-selective-supervised-distributional/) — Stephen Roller, Katrin Erk, Gemma Boleda · 2014 · 217 citations - [Learning Word-Class Lattices for Definition and Hypernym Extraction](https://md.research.elamur.ai/free/en/papers/navigli-2010-learning-word-class-lattices-definition/) — Roberto Navigli, Paola Velardi · 2010 · 201 citations - [Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings](https://md.research.elamur.ai/free/en/papers/gonzales-2017-improving-word-sense-disambiguation-neural/) — Annette Rios Gonzales, Laura Mascarell, Rico Sennrich · 2017 · 185 citations - [Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut](https://md.research.elamur.ai/free/en/papers/schneider-2014-discriminative-lexical-semantic-segmentation-gaps/) — Nathan Schneider, Emily Danchik, Chris Dyer, et al. · 2014 · 159 citations - [Diachronic Usage Relatedness (DURel): A Framework for the Annotation of Lexical Semantic Change](https://md.research.elamur.ai/free/en/papers/schlechtweg-2018-diachronic-usage-relatedness-durel-framework/) — Dominik Schlechtweg, Sabine Schulte im Walde, Stefanie Eckmann · 2018 · 158 citations - [Structural Ambiguity and Lexical Relations](https://md.research.elamur.ai/free/en/papers/hindle-1993-structural-ambiguity-lexical-relations/) — Donald Hindle, Mats Rooth · 1993 · 146 citations - [Identifying hypernyms in distributional semantic spaces](https://md.research.elamur.ai/free/en/papers/lenci-2012-identifying-hypernyms-distributional-semantic-spaces/) — Alessandro Lenci, Giulia Benotto · 2012 · 65 citations - [Reflexive verbs in Hebrew: Deep unaccusativity meets lexical semantics](https://md.research.elamur.ai/free/en/papers/lingbulk-2017-kastner-reflexive-verbs-hebrew-deep-unaccusativity/) — Itamar Kastner · 2017 · 52 citations - [A Compact Arabic Lexical Semantics Language Resource Based on the Theory of Semantic Fields](https://md.research.elamur.ai/free/en/papers/attia-2008-compact-arabic-lexical-semantics-language/) — Mohamed Attia, Mohsen Rashwan, Ahmed Ragheb, et al. · 2008 · 27 citations **1** [2](https://md.research.elamur.ai/free/en/topics/lexical-semantics/page/2/) [next →](https://md.research.elamur.ai/free/en/topics/lexical-semantics/page/2/) - [← previous topic Legal AI](https://md.research.elamur.ai/free/en/topics/legal-ai/) - [next topic → Low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) - [↑ back to topics](https://md.research.elamur.ai/free/en/topics/)