BIOMEDICAL WORD SENSE DISAMBIGUATION WITH NEURAL WORD AND CONCEPT EMBEDDINGS

Addressing ambiguity issues is an important step in natural language processing (NLP) pipelines designed for information extraction and knowledge discovery. This problem is also common in biomedicine where NLP applications have become indispensable to exploit latent information from biomedical literature and clinical narratives from electronic medical records. In this thesis, we propose an ensemble model that employs recent advances in neural word embeddings along with knowledge based approaches to build a biomedical word sense disambiguation (WSD) system. Specifically, our system identities the correct sense from a given set of candidates for each ambiguous word when presented in its context (surrounding words). We use the MSH WSD dataset, a well known public dataset consisting of 203 ambiguous terms each with nearly 200 different instances and an average of two candidate senses represented by concepts in the unified medical language system (UMLS). We employ a popular biomedical concept, Our linear time (in terms of number of senses and context length) unsupervised and knowledge based approach improves over the state-of-the-art methods by over 3% in accuracy. A more expensive approach based on the k-nearest neighbor framework improves over prior best results by 5% in accuracy. Our results demonstrate that recent advances in neural dense word vector representations offer excellent potential for solving biomedical WSD

Thematic Annotation: extracting concepts out of documents

Contrarily to standard approaches to topic annotation, the technique used in this work does not centrally rely on some sort of -- possibly statistical -- keyword extraction. In fact, the proposed annotation algorithm uses a large scale semantic database -- the EDR Electronic Dictionary -- that provides a concept hierarchy based on hyponym and hypernym relations. This concept hierarchy is used to generate a synthetic representation of the document by aggregating the words present in topically homogeneous document segments into a set of concepts best preserving the document's content. This new extraction technique uses an unexplored approach to topic selection. Instead of using semantic similarity measures based on a semantic resource, the later is processed to extract the part of the conceptual hierarchy relevant to the document content. Then this conceptual hierarchy is searched to extract the most relevant set of concepts to represent the topics discussed in the document. Notice that this algorithm is able to extract generic concepts that are not directly present in the document.Comment: Technical report EPFL/LIA. 81 pages, 16 figure

WikiM: Metapaths based Wikification of Scientific Abstracts

In order to disseminate the exponential extent of knowledge being produced in the form of scientific publications, it would be best to design mechanisms that connect it with already existing rich repository of concepts -- the Wikipedia. Not only does it make scientific reading simple and easy (by connecting the involved concepts used in the scientific articles to their Wikipedia explanations) but also improves the overall quality of the article. In this paper, we present a novel metapath based method, WikiM, to efficiently wikify scientific abstracts -- a topic that has been rarely investigated in the literature. One of the prime motivations for this work comes from the observation that, wikified abstracts of scientific documents help a reader to decide better, in comparison to the plain abstracts, whether (s)he would be interested to read the full article. We perform mention extraction mostly through traditional tf-idf measures coupled with a set of smart filters. The entity linking heavily leverages on the rich citation and author publication networks. Our observation is that various metapaths defined over these networks can significantly enhance the overall performance of the system. For mention extraction and entity linking, we outperform most of the competing state-of-the-art techniques by a large margin arriving at precision values of 72.42% and 73.8% respectively over a dataset from the ACL Anthology Network. In order to establish the robustness of our scheme, we wikify three other datasets and get precision values of 63.41%-94.03% and 67.67%-73.29% respectively for the mention extraction and the entity linking phase