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Bridging Natural Language Processing and Psycholinguistics: computationally grounded semantic similarity datasets for Basque and Spanish

arXiv 2304.09616 ↗First posted 19 Apr 2023cs.CLcited by 0

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Abstract (arXiv)

We present a computationally-grounded word similarity dataset based on two well-known Natural Language Processing resources; text corpora and knowledge bases. This dataset aims to fulfil a gap in psycholinguistic research by providing a variety of quantifications of semantic similarity in an extensive set of noun pairs controlled by variables that play a significant role in lexical processing. The dataset creation has consisted in three steps, 1) computing four key psycholinguistic features for each noun; concreteness, frequency, semantic and phonological neighbourhood density; 2) pairing nouns across these four variables; 3) for each noun pair, assigning three types of word similarity measurements, computed out of text, Wordnet and hybrid embeddings. The present dataset includes noun pairs' information in Basque and European Spanish, but further work intends to extend it to more languages.

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References · 25

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  4. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  5. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  6. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  7. 2019Cross-lingual Language Model Pretrainingcited by 1,088
  8. 2016Neural Architectures for Named Entity Recognitioncited by 548
  9. 2017Six Challenges for Neural Machine Translationcited by 334
  10. 2017Unsupervised Machine Translation Using Monolingual Corpora Onlycited by 272
  11. 2017Advances in Pre-Training Distributed Word Representationscited by 207
  12. 2014SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimationcited by 170
13 more
  1. 2018A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddingscited by 145
  2. 2019Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)cited by 128
  3. 2020Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Researchcited by 67
  4. 2016Deep Reinforcement Learning for Mention-Ranking Coreference Modelscited by 63
  5. 2018Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networkscited by 36
  6. 2019CogniVal: A Framework for Cognitive Word Embedding Evaluationcited by 27
  7. 2018A Survey of Word Embeddings Evaluation Methodscited by 26
  8. 2022Does Corpus Quality Really Matter for Low-Resource Languages?cited by 11
  9. 2018Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddingscited by 10
  10. 2016Ultradense Word Embeddings by Orthogonal Transformationcited by 8
  11. 2017Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activitycited by 8
  12. 2020A Common Semantic Space for Monolingual and Cross-Lingual Meta-Embeddingscited by 2
  13. 2018Bilingual Embeddings with Random Walks over Multilingual Wordnetscited by 1

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