Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical Evaluation Resource

• URL: http://arxiv.org/abs/2409.20524v1
• Date: Mon, 30 Sep 2024 17:22:33 GMT
• Title: Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical Evaluation Resource
• Authors: Pablo Ortega, Jordi Luque, Luis Lamiable, Rodrigo López, Richard Benjamins,
• Abstract summary: A lexical meaning of a word in context can be determined automatically by Word Sense Disambiguation (WSD) algorithms. This study introduces a new resource for Spanish WSD. It includes a sense inventory and a lexical dataset sourced from the Diccionario de la Lengua Espanola.
• Score: 2.7775559369441964
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Human language, while aimed at conveying meaning, inherently carries ambiguity. It poses challenges for speech and language processing, but also serves crucial communicative functions. Efficiently solve ambiguity is both a desired and a necessary characteristic. The lexical meaning of a word in context can be determined automatically by Word Sense Disambiguation (WSD) algorithms that rely on external knowledge often limited and biased toward English. When adapting content to other languages, automated translations are frequently inaccurate and a high degree of expert human validation is necessary to ensure both accuracy and understanding. The current study addresses previous limitations by introducing a new resource for Spanish WSD. It includes a sense inventory and a lexical dataset sourced from the Diccionario de la Lengua Espa\~nola which is maintained by the Real Academia Espa\~nola. We also review current resources for Spanish and report metrics on them by a state-of-the-art system.

Related papers

• Evaluating Contextualized Representations of (Spanish) Ambiguous Words: A New Lexical Resource and Empirical Analysis [2.2530496464901106]
  We evaluate semantic representations of Spanish ambiguous nouns in context in a suite of Spanish-language monolingual and multilingual BERT-based models. We find that various BERT-based LMs' contextualized semantic representations capture some variance in human judgments but fall short of the human benchmark.
  arXiv  Detail & Related papers  (2024-06-20T18:58:11Z)
• A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation [0.0]
  This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP) It outlines diverse approaches ranging from deep learning techniques to leveraging lexical resources and knowledge graphs like WordNet. The research identifies persistent challenges in the field, such as the scarcity of sense annotated corpora and the complexity of informal clinical texts.
  arXiv  Detail & Related papers  (2024-03-24T12:58:48Z)
• We're Afraid Language Models Aren't Modeling Ambiguity [136.8068419824318]
  Managing ambiguity is a key part of human language understanding. We characterize ambiguity in a sentence by its effect on entailment relations with another sentence. We show that a multilabel NLI model can flag political claims in the wild that are misleading due to ambiguity.
  arXiv  Detail & Related papers  (2023-04-27T17:57:58Z)
• A Linguistic Investigation of Machine Learning based Contradiction Detection Models: An Empirical Analysis and Future Perspectives [0.34998703934432673]
  We analyze two Natural Language Inference data sets with respect to their linguistic features. The goal is to identify those syntactic and semantic properties that are particularly hard to comprehend for a machine learning model.
  arXiv  Detail & Related papers  (2022-10-19T10:06:03Z)
• Multilingual Word Sense Disambiguation with Unified Sense Representation [55.3061179361177]
  We propose building knowledge and supervised-based Multilingual Word Sense Disambiguation (MWSD) systems. We build unified sense representations for multiple languages and address the annotation scarcity problem for MWSD by transferring annotations from rich-sourced languages to poorer ones. Evaluations of SemEval-13 and SemEval-15 datasets demonstrate the effectiveness of our methodology.
  arXiv  Detail & Related papers  (2022-10-14T01:24:03Z)
• Monolingual alignment of word senses and definitions in lexicographical resources [0.0]
  The focus of this thesis is broadly on the alignment of lexicographical data, particularly dictionaries. The first task aims to find an optimal alignment given the sense definitions of a headword in two different monolingual dictionaries. This benchmark can be used for evaluation purposes of word-sense alignment systems.
  arXiv  Detail & Related papers  (2022-09-06T13:09:52Z)
• AUTOLEX: An Automatic Framework for Linguistic Exploration [93.89709486642666]
  We propose an automatic framework that aims to ease linguists' discovery and extraction of concise descriptions of linguistic phenomena. Specifically, we apply this framework to extract descriptions for three phenomena: morphological agreement, case marking, and word order. We evaluate the descriptions with the help of language experts and propose a method for automated evaluation when human evaluation is infeasible.
  arXiv  Detail & Related papers  (2022-03-25T20:37:30Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)
• Speakers Fill Lexical Semantic Gaps with Context [65.08205006886591]
  We operationalise the lexical ambiguity of a word as the entropy of meanings it can take. We find significant correlations between our estimate of ambiguity and the number of synonyms a word has in WordNet. This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.
  arXiv  Detail & Related papers  (2020-10-05T17:19:10Z)
• Reinforcement learning of minimalist grammars [0.5862282909017474]
  State-of-the-art language technology scans the acoustically analyzed speech signal for relevant keywords. Words are then inserted into semantic slots to interpret the user's intent. A mental lexicon must be acquired by a cognitive agent during interaction with its users.
  arXiv  Detail & Related papers  (2020-04-30T14:25:58Z)
• Word Sense Disambiguation for 158 Languages using Word Embeddings Only [80.79437083582643]
  Disambiguation of word senses in context is easy for humans, but a major challenge for automatic approaches. We present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings.
  arXiv  Detail & Related papers  (2020-03-14T14:50:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.