Improving approximation of domain-focused, corpus-based, lexical semantic relatedness

Semantic relatedness is a measure that quantifies the strength of a semantic link between two concepts. Often, it can be efficiently approximated with methods that operate on words, which represent these concepts. Approximating semantic relatedness between texts and concepts represented by these texts is an important part of many text and knowledge processing tasks of crucial importance in many domain-specific scenarios. The problem of most state-of-the-art methods for calculating domain-specific semantic relatedness is their dependence on highly specialized, structured knowledge resources, which makes these methods poorly adaptable for many usage scenarios. On the other hand, the domain knowledge in the fields such as Life Sciences has become more and more accessible, but mostly in its unstructured form - as texts in large document collections, which makes its use more challenging for automated processing. In this dissertation, three new corpus-based methods for approximating domain-specific textual semantic relatedness are presented and evaluated with a set of standard benchmarks focused on the field of biomedicine. Nonetheless, the proposed measures are general enough to be adapted to other domain-focused scenarios. The evaluation involves comparisons with other relevant state-of-the-art measures for calculating semantic relatedness and the results suggest that the methods presented here perform comparably or better than other approaches. Additionally, the dissertation also presents an experiment, in which one of the proposed methods is applied within an ontology matching system, DisMatch. The performance of the system was evaluated externally on a biomedically themed ‘Phenotype’ track of the Ontology Alignment Evaluation Initiative 2016 campaign. The results of the track indicate, that the use distributional semantic relatedness for ontology matching is promising, as the system presented in this thesis did stand out in detecting correct mappings that were not detected by any other systems participating in the track. The work presented in the dissertation indicates an improvement achieved w.r.t. the stat-of-the-art through the domain adapted use of the distributional principle (i.e. the presented methods are corpus-based and do not require additional resources). The ontology matching experiment showcases practical implications of the presented theoretical body of work

Creación de datos multilingües para diversos enfoques basados en corpus en el ámbito de la traducción y la interpretación

Accordingly, this research work aims at exploiting and developing new technologies and methods to better ascertain not only translators’ and interpreters’ needs, but also professionals’ and ordinary people’s on their daily tasks, such as corpora and terminology compilation and management. The main topics covered by this work relate to Computational Linguistics (CL), Natural Language Processing (NLP), Machine Translation (MT), Comparable Corpora, Distributional Similarity Measures (DSM), Terminology Extraction Tools (TET) and Terminology Management Tools (TMT). In particular, this work examines three main questions: 1) Is it possible to create a simpler and user-friendly comparable corpora compilation tool? 2) How to identify the most suitable TMT and TET for a given translation or interpreting task? 3) How to automatically assess and measure the internal degree of relatedness in comparable corpora? This work is composed of thirteen peer-reviewed scientific publications, which are included in Appendix A, while the methodology used and the results obtained in these studies are summarised in the main body of this document. Fecha de lectura de Tesis Doctoral: 22 de noviembre 2019Corpora are playing an increasingly important role in our multilingual society. High-quality parallel corpora are a preferred resource in the language engineering and the linguistics communities. Nevertheless, the lack of sufficient and up-to-date parallel corpora, especially for narrow domains and poorly-resourced languages is currently one of the major obstacles to further advancement across various areas like translation, language learning and, automatic and assisted translation. An alternative is the use of comparable corpora, which are easier and faster to compile. Corpora, in general, are extremely important for tasks like translation, extraction, inter-linguistic comparisons and discoveries or even to lexicographical resources. Its objectivity, reusability, multiplicity and applicability of uses, easy handling and quick access to large volume of data are just an example of their advantages over other types of limited resources like thesauri or dictionaries. By a way of example, new terms are coined on a daily basis and dictionaries cannot keep up with the rate of emergence of new terms

Query Expansion for Survey Question Retrieval in the Social Sciences

In recent years, the importance of research data and the need to archive and to share it in the scientific community have increased enormously. This introduces a whole new set of challenges for digital libraries. In the social sciences typical research data sets consist of surveys and questionnaires. In this paper we focus on the use case of social science survey question reuse and on mechanisms to support users in the query formulation for data sets. We describe and evaluate thesaurus- and co-occurrence-based approaches for query expansion to improve retrieval quality in digital libraries and research data archives. The challenge here is to translate the information need and the underlying sociological phenomena into proper queries. As we can show retrieval quality can be improved by adding related terms to the queries. In a direct comparison automatically expanded queries using extracted co-occurring terms can provide better results than queries manually reformulated by a domain expert and better results than a keyword-based BM25 baseline.Comment: to appear in Proceedings of 19th International Conference on Theory and Practice of Digital Libraries 2015 (TPDL 2015