How word-embedding methods improve information extraction and can be used for multilingual approaches

Expanding entity sets and extracting relations are key tasks in natural language processing (NLP), which is accomplished in various approaches. Recent successful attempts are all using word-embeddings like the ones presented by Mikolov et al. While most work concentrates on how to improve these tasks in general without considering a specific domain, it is of interest how to achieve even higher precisions when focusing on a specific domain and optimizing the methods towards a single purpose. Therefore this thesis suggests methods and adjustments to optimize the proposals for entity set expansion for the domain of drugs. While this is the main purpose of this thesis, it will also present a novel idea, how to improve the precision in relation extraction by using word-embeddings, which could be combined with existing successful relation extraction methods. And finally another key aspect of many international companies is tagged, by presenting a solution for multilingual information extraction system (IES), which is capable of preprocessing text of multiple languages, expanding entity sets independent of the language used and extracting relations on the texts

Automated Knowledge Base Extension Using Open Information

Open Information Extractions (OIE) (like Nell, Reverb) frameworks provide us with domain independent facts in natural language forms containing knowledge from varied sources. Extraction mechanisms for structured knowledge bases (KB) (like DBpedia, Yago) often fail to retrieve such facts due to its resource specific extraction schemes. Hence, the structured KBs can extend themselves by augmenting their coverage with the facts discovered by OIE systems. This possibility motivates us to integrate these two genres of extractions into one interactive framework. In this work, we present a complete, ontology independent, generalized architecture for achieving this integration. Our proposed solution is modularized which solves a specific set of tasks: (1) mapping subject and object terms from OIE facts to KB instances (2) mapping the OIE relational phrases to object properties defined in the KB. Furthermore, in an open extraction setting identical semantic relationships can be represented by different surface forms, making it necessary to group them together. To solve this problem, (3) we propose the use of markov clustering to cluster OIE relations. Key to our approach lies in exploiting the inherent dependancies between relations and its arguments. This makes our approach completely context agnostic and generally applicable. We evaluated our method on the two state of the art extraction systems, achieving over 85% precision on instance mappings and over 90% for the relation mappings. We also created a distant supervision based gold standard for the purpose and the data has been released as part of this work. Furthermore, we analyze the effect of clustering and empirically show its effectiveness as a relation mapping technique over other techniques. Overall, our work positions itself on the intersection of information extraction, ontology mapping and reasoning