A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment
Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.
Code (1)
Similar Papers 제목 키워드 기반
Unsupervised Word Sense Disambiguation with Multilingual Representations
In this paper we investigate the role of multilingual features in improving word sense disambiguation. In particular, we explore the use of semantic clues derived from context translation to enrich the intended sense and…
TranslationWord Sense DisambiguationMultilingual Substitution-based Word Sense Induction
Word Sense Induction (WSI) is the task of discovering senses of an ambiguous word by grouping usages of this word into clusters corresponding to these senses. Many approaches were proposed to solve WSI in English and a f…
Language ModelingLanguage ModellingWord Sense InductionExploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy
This paper presents a multilingual study of word meaning representations in context. We assess the ability of both static and contextualized models to adequately represent different lexical-semantic relations, such as ho…
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP tasks. A recent line of work uses bilingual (two languages) corpora to learn a different vector for each sense o…
Representation LearningWord EmbeddingsBilingual Learning of Multi-sense Embeddings with Discrete Autoencoders
We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model consists of an encoder, which uses monolingual and bilingual context (i.e. a parallel senten…
DecoderSentenceWord Embeddings