WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations MT Pilehvar, J Camacho-Collados NAACL 2019, 2019 | 449 | 2019 |
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning J Camacho-Collados, MT Pilehvar Journal of Artificial Intelligence Research (JAIR), 2018 | 430 | 2018 |
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity I Iacobacci, MT Pilehvar, R Navigli ACL 2015, 2015 | 370 | 2015 |
Embeddings for Word Sense Disambiguation: An Evaluation Study I Iacobacci, MT Pilehvar, R Navigli ACL 2016, 2016 | 366 | 2016 |
Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity MT Pilehvar, D Jurgens, R Navigli ACL 2013, 2013 | 253 | 2013 |
NASARI: Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities J Camacho-Collados, MT Pilehvar, R Navigli Artificial Intelligence (AIJ) 240, 2016 | 243 | 2016 |
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis J Camacho-Collados, MT Pilehvar BlackboxNLP (EMNLP 2018), 2018 | 222 | 2018 |
Semeval-2017 Task 2: Multilingual and Cross-Lingual Semantic Word Similarity J Camacho-Collados, MT Pilehvar, N Collier, R Navigli SemEval 2017, 2017 | 183 | 2017 |
What’s missing in geographical parsing? M Gritta, MT Pilehvar, N Limsopatham, N Collier Language Resources and Evaluation 52 (2), 2018 | 154 | 2018 |
NASARI: a novel approach to a semantically-aware representation of items J Camacho-Collados, MT Pilehvar, R Navigli NAACL 2015, 2015 | 132 | 2015 |
From senses to texts: An all-in-one graph-based approach for measuring semantic similarity MT Pilehvar, R Navigli Artificial Intelligence (AIJ) 228, 2015 | 130 | 2015 |
Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning MT Pilehvar, J Camacho-Collados Synthesis Lectures on Human Language Technologies, 2020 | 115 | 2020 |
De-Conflated Semantic Representations MT Pilehvar, N Collier EMNLP 2016, 2016 | 113 | 2016 |
Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter C Conforti, J Berndt, MT Pilehvar, C Giannitsarou, F Toxvaerd, N Collier ACL 2020, 2020 | 102 | 2020 |
SemEval-2014 Task 3: Cross-Level Semantic Similarity D Jurgens, MT Pilehvar, R Navigli SemEval 2014, 2014 | 88 | 2014 |
Mapping text to knowledge graph entities using multi-sense LSTMs D Kartsaklis, MT Pilehvar, N Collier EMNLP 2018, 2018 | 82 | 2018 |
Analysis and evaluation of language models for word sense disambiguation D Loureiro, K Rezaee, MT Pilehvar, J Camacho-Collados Computational Linguistics 47 (2), 387-443, 2021 | 79* | 2021 |
SemEval-2016 Task 14: Semantic Taxonomy Enrichment D Jurgens, MT Pilehvar SemEval 2016, 2016 | 76 | 2016 |
Which Melbourne? Augmenting Geocoding with Maps M Gritta, MT Pilehvar, N Collier ACL 2018, 1285-1296, 2018 | 74 | 2018 |
A Framework for the Construction of Monolingual and Cross-lingual Word Similarity Datasets J Camacho-Collados, MT Pilehvar, R Navigli ACL 2015, 2015 | 73* | 2015 |