Can multilinguality improve Biomedical Word Sense Disambiguation?
Ambiguity in the biomedical domain represents a major issue when performing Natural Language Processing tasks over the huge amount of available information in the field. For this reason, Word Sense Disambiguation is critical for achieving accurate systems able to tackle complex tasks such as information extraction, summarization or document classification. In this work we explore whether multilinguality can help to solve the problem of ambiguity, and the conditions required for a system to improve the results obtained by monolingual approaches. Also, we analyze the best ways to generate those useful multilingual resources, and study different languages and sources of knowledge. The proposed system, based on co-occurrence graphs containing biomedical concepts and textual information, is evaluated on a test dataset frequently used in biomedicine. We can conclude that multilingual resources are able to provide a clear improvement of more than 7% compared to monolingual approaches, for graphs built from a small number of documents. Also, empirical results show that automatically translated resources are a useful source of information for this particular task.
NLP & IR Group, Dpto. Lenguajes y Sistemas Informáticos, Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain. Electronic address: aduque@lsi.uned.es.
Andres
A
Duque
NLP & IR Group, Dpto. Lenguajes y Sistemas Informáticos, Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain. Electronic address: juaner@lsi.uned.es.
Juan
J
Martinez-Romo
NLP & IR Group, Dpto. Lenguajes y Sistemas Informáticos, Universidad Nacional de Educación a Distancia (UNED), Madrid 28040, Spain. Electronic address: lurdes@lsi.uned.es.
Lourdes
L
Araujo
D000465:Algorithms; D057225:Data Mining; D006801:Humans; D051188:Knowledge Bases; D009323:Natural Language Processing; D017432:Unified Medical Language System
Biomedical Word Sense Disambiguation; Graph-based systems; Multilinguality; Parallel and comparable corpora; Unified Medical Language System; Unsupervised systems