Search Results for author: Luis Espinosa Anke

Found 13 papers, 6 papers with code

Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions

no code implementations COLING (CogALex) 2020 Mireia Roig Mirapeix, Luis Espinosa Anke, Jose Camacho-Collados

Textual definitions constitute a fundamental source of knowledge when seeking the meaning of words, and they are the cornerstone of lexical resources like glossaries, dictionaries, encyclopedia or thesauri.

Definition Extraction

CollFrEn: Rich Bilingual English–French Collocation Resource

1 code implementation COLING (MWE) 2020 Beatriz Fisas, Luis Espinosa Anke, Joan Codina-Filbá, Leo Wanner

Collocations in the sense of idiosyncratic lexical co-occurrences of two syntactically bound words traditionally pose a challenge to language learners and many Natural Language Processing (NLP) applications alike.

Machine Translation Relation Classification +3

Combining BERT with Static Word Embeddings for Categorizing Social Media

no code implementations EMNLP (WNUT) 2020 Israa Alghanmi, Luis Espinosa Anke, Steven Schockaert

A particularly striking example is the performance of AraBERT, an LM for the Arabic language, which is successful in categorizing social media posts in Arabic dialects, despite only having been trained on Modern Standard Arabic.

Word Embeddings

Distilling Hypernymy Relations from Language Models: On the Effectiveness of Zero-Shot Taxonomy Induction

no code implementations10 Feb 2022 Devansh Jain, Luis Espinosa Anke

In this paper, we analyze zero-shot taxonomy learning methods which are based on distilling knowledge from language models via prompting and sentence scoring.

Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection

1 code implementation ACL (RepL4NLP) 2021 Yixiao Wang, Zied Bouraoui, Luis Espinosa Anke, Steven Schockaert

Second, rather than learning a word vector directly, we use a topic model to partition the contexts in which words appear, and then learn different topic-specific vectors for each word.

Word Embeddings

Evaluating language models for the retrieval and categorization of lexical collocations

1 code implementation EACL 2021 Luis Espinosa Anke, Joan Codina-Filba, Leo Wanner

We first construct a dataset of apparitions of lexical collocations in context, categorized into 17 representative semantic categories.

Collocation Classification with Unsupervised Relation Vectors

1 code implementation ACL 2019 Luis Espinosa Anke, Steven Schockaert, Leo Wanner

Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words.

Classification General Classification +2

Example-based Acquisition of Fine-grained Collocation Resources

no code implementations LREC 2016 Sara Rodr{\'\i}guez-Fern{\'a}ndez, Roberto Carlini, Luis Espinosa Anke, Leo Wanner

Collocations such as {``}heavy rain{''} or {``}make [a] decision{''}, are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted, depending on the base.

Word Embeddings

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