DBnary: Wiktionary as a Lemon-based multilingual lexical resource in RDF

Abstract

Contributive resources, such as Wikipedia, have proved to be valuable to Natural Language Processing or multilingual Information Retrieval applications. This work focusses on Wiktionary, the dictionary part of the resources sponsored by the Wikimedia foundation. In this article, we present our extraction of multilingual lexical data from Wiktionary data and to provide it to the community as a Multilingual Lexical Linked Open Data (MLLOD). This lexical resource is structured using the LEMON Model.

SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

“…CompiLIG The best Spanish-English performance on SNLI sentences was achieved by CompiLIG using features including: cross-lingual conceptual similarity using DBNary (Serasset, 2015), cross-language MultiVec word embeddings (Berard et al, 2016), and Brychcin and Svoboda (2016)'s improvements to Sultan et al (2015)'s method. (Nagoudi et al, 2017) Using only weighted word embeddings, LIM-LIG took second place on Arabic.…”

CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity

“…A bag-of-words S from each sentence S is built, by filtering stop words and by using a function that returns for a given word all its possible translations. These translations are jointly given by a linked lexical resource, DBNary (Sérasset, 2015), and by cross-lingual word embeddings. More precisely, we use the top 10 closest words in the embeddings model and all the available translations from DBNary to build the bag-of-words of a word.…”

Deep Investigation of Cross-Language Plagiarism Detection Methods

“…We reuse the idea of Pataki (2012) which, for each sentence, build a bag-ofwords by getting all the available translations of each word of the sentence. For that, we use a linked lexical resource called DBNary (Sérasset, 2015). The bag-of-words of a sentence is the merge of the bag-of-words of the words of the sentence.…”

“…We use the Muhr et al (2010)'s implementation which consists in replacing each word of one text by its most likely translations in the language of the other text, leading to a bags-of-words. We use DBNary (Sérasset, 2015) to get the translations. The metric used to compare two texts is a monolingual matching based on strict intersection of bags-of-words.…”