Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

Dutch hypernym detection : does decompounding help?

This research presents experiments carried out to improve the precision and recall of Dutch hypernym detection. To do so, we applied a data-driven semantic relation finder that starts from a list of automatically extracted domain-specific terms from technical corpora, and generates a list of hypernym relations between these terms. As Dutch technical terms often consist of compounds written in one orthographic unit, we investigated the impact of a decompounding module on the performance of the hypernym detection system. In addition, we also improved the precision of the system by designing filters taking into account statistical and linguistic information. The experimental results show that both the precision and recall of the hypernym detection system improved, and that the decompounding module is especially effective for hypernym detection in Dutch

Evaluation of automatic hypernym extraction from technical corpora in English and Dutch

In this research, we evaluate different approaches for the automatic extraction of hypernym relations from English and Dutch technical text. The detected hypernym relations should enable us to semantically structure automatically obtained term lists from domain- and user-specific data. We investigated three different hypernymy extraction approaches for Dutch and English: a lexico-syntactic pattern-based approach, a distributional model and a morpho-syntactic method. To test the performance of the different approaches on domain-specific data, we collected and manually annotated English and Dutch data from two technical domains, viz. the dredging and financial domain. The experimental results show that especially the morpho-syntactic approach obtains good results for automatic hypernym extraction from technical and domain-specific texts

Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1