Resolving API Mentions in Informal Documents

Species Disambiguation for Biomedical Term Identification

An important task in information extraction (IE) from biomedical articles is term identification (TI), which concerns linking entity mentions (e.g., terms denoting proteins) in text to unambiguous identifiers in standard databases (e.g., RefSeq). Previous work on TI has focused on species-specific documents. However, biomedical documents, especially full-length articles, often talk about entiti...

On Developing Extraction Rules for Mining Informal Scientific References from Altmetric Data Sources

Altmetrics measure scientific impact outside of traditional scientific literature. We identify mentions of scientific research or entities like researchers, academic or research organizations in a corpus containing blogs, articles, news items etc. We manually analysed the corpus for patterns of such informal mentions and then applied text mining techniques by developing extraction rules for min...

A Semantic-Based Approach for Artist Similarity

This paper describes and evaluates a method for computing artist similarity from a set of artist biographies. The proposed method aims at leveraging semantic information present in these biographies, and can be divided in three main steps, namely: (1) entity linking, i.e. detecting mentions to named entities in the text and linking them to an external knowledge base; (2) deriving a knowledge re...

Resolving Event Noun Phrases to Their Verbal Mentions

Entity Linking with Effective Acronym Expansion, Instance Selection, and Topic Modeling

Entity linking maps name mentions in the documents to entries in a knowledge base through resolving the name variations and ambiguities. In this paper, we propose three advancements for entity linking. Firstly, expanding acronyms can effectively reduce the ambiguity of the acronym mentions. However, only rule-based approaches relying heavily on the presence of text markers have been used for en...