Multilingual Lexical Resources Research Articles

Converging Lexicography and Neology

Abstract: This introduction summarizes general issues combining lexicography and neology in the context of the Globalex Workshop on Lexicography and Neology series. We present each of the six papers composing this Special Issue, featuring two Slavic languages (Czech and Slovak) and two Romance ones (Brazilian Portuguese and Spanish in its European and Latin American varieties) and their diverse lexicographic research and representation, in specialized dictionaries of neologisms or general language ones, in monolingual, bilingual and multilingual lexical resources, and in print and digital dictionaries.

LessLex: Linking Multilingual Embeddings to SenSe Representations of LEXical Items

We present LESSLEX, a novel multilingual lexical resource. Different from the vast majority of existing approaches, we ground our embeddings on a sense inventory made available from the BabelNet semantic network. In this setting, multilingual access is governed by the mapping of terms onto their underlying sense descriptions, such that all vectors co-exist in the same semantic space. As a result, for each term we have thus the “blended” terminological vector along with those describing all senses associated to that term. LESSLEX has been tested on three tasks relevant to lexical semantics: conceptual similarity, contextual similarity, and semantic text similarity. We experimented over the principal data sets for such tasks in their multilingual and crosslingual variants, improving on or closely approaching state-of-the-art results. We conclude by arguing that LESSLEX vectors may be relevant for practical applications and for research on conceptual and lexical access and competence.

Multilingual Approach to Word Sense Disambiguation Using word2vec and Sense Embedding With Semantic Cloud

Word Sense Disambiguation(WSD) is an open problem in the area of Natural language processing. Many WSD systems uses knowledge based unsupervised approach as it bares us from using tagged corpus. But this approach depends extensively on knowledge. Enhancing the knowledge for ambiguity resolution is one of the important issues in WSD. Although Multilingual lexical resource is an asset for knowledge based systems(KBS), many KBS have not yield satisfactory results due to lack of sufficient information required for disambiguation. Multilingual information can help boost the performance of WSD in various languages. Combining lexical semantic information from different languages helps improve WSD task. We make use of Babel Net, a huge multilingual resource provider to attain the task and design a semantic word cloud to provide high performance disambiguation by imposing additional knowledge.

Connective-Lex: A Web-Based Multilingual Lexical Resource for Connectives

In this paper, we present a tangible outcome of the TextLink network: a joint online database project displaying and linking existing and newly-created lexicons of discourse connectives in multiple languages. We discuss the definition and demarcation of the class of connectives that should be included in such a resource, and present the syntactic, semantic/pragmatic, and lexicographic information we collected. Further, the technical implementation of the database and the search functionality are presented. We discuss how the multilingual integration of several connective lexicons provides added value for linguistic researchers and other users interested in connectives, by allowing crosslinguistic comparison and a direct linking between discourse relational devices in different languages. Finally, we provide pointers for possible future extensions both in breadth (i.e., by adding lexicons for additional languages) and depth (by extending the information provided for each connective item and by strengthening the crosslinguistic links).

Improving Machine Translation through Linked Data

Abstract With the ever increasing availability of linked multilingual lexical resources, there is a renewed interest in extending Natural Language Processing (NLP) applications so that they can make use of the vast set of lexical knowledge bases available in the Semantic Web. In the case of Machine Translation, MT systems can potentially benefit from such a resource. Unknown words and ambiguous translations are among the most common sources of error. In this paper, we attempt to minimise these types of errors by interfacing Statistical Machine Translation (SMT) models with Linked Open Data (LOD) resources such as DBpedia and BabelNet. We perform several experiments based on the SMT system Moses and evaluate multiple strategies for exploiting knowledge from multilingual linked data in automatically translating named entities. We conclude with an analysis of best practices for multilingual linked data sets in order to optimise their benefit to multilingual and cross-lingual applications.

Effectiveness of Automatic Translations for Cross-Lingual Ontology Mapping

Accessing or integrating data lexicalized in different languages is a challenge. Multilingual lexical resources play a fundamental role in reducing the language barriers to map concepts lexicalized in different languages. In this paper we present a large-scale study on the effectiveness of automatic translations to support two key cross-lingual ontology mapping tasks: the retrieval of candidate matches and the selection of the correct matches for inclusion in the final alignment. We conduct our experiments using four different large gold standards, each one consisting of a pair of mapped wordnets, to cover four different families of languages. We categorize concepts based on their lexicalization (type of words, synonym richness, position in a subconcept graph) and analyze their distributions in the gold standards. Leveraging this categorization, we measure several aspects of translation effectiveness, such as word-translation correctness, word sense coverage, synset and synonym coverage. Finally, we thoroughly discuss several findings of our study, which we believe are helpful for the design of more sophisticated cross-lingual mapping algorithms.

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

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. This data, called DBnary, is registered at http://thedatahub.org/dataset/dbnary.

Low Cost Construction of a Multilingual Lexicon from Bilingual Lists

Manually constructing multilingual translation lexicons can be very costly, both in terms of time and human effort. Although there have been many efforts at (semi-)automatically merging bilingual machine readable dictionaries to produce a multilingual lexicon, most of these approaches place quite specific requirements on the input bilingual resources. Unfortunately, not all bilingual dictionaries fulfil these criteria, especially in the case of under-resourced language pairs. We describe a low cost method for constructing a multilingual lexicon using only simple lists of bilingual translation mappings. The method is especially suitable for under-resourced language pairs, as such bilingual resources are often freely available and easily obtainable from the Internet, or digitised from simple, conventional paper-based dictionaries. The precision of random samples of the resultant multilingual lexicon is around 0.70-0.82, while coverage for each language, precision and recall can be controlled by varying threshold values. Given the very simple input resources, our results are encouraging, especially in incorporating under-resourced languages into multilingual lexical resources.

Lexical systems: graph models of natural language lexicons

We introduce a new type of lexical structure called lexical system, an interoperable model that can feed both monolingual and multilingual language resources. We begin with a formal characterization of lexical systems as simple directed graphs, solely made up of nodes corresponding to lexical entities and links. To illustrate our approach, we present data borrowed from a lexical system that has been generated from the French DiCo database. We later explain how the compilation of the original dictionary-like database into a net-like one has been made possible. Finally, we discuss the potential of the proposed lexical structure for designing multilingual lexical resources.