student profile: Mr Xiang Dai


Thesis work

Thesis title: Recognizing Complex Entity Mentions

Supervisors: Joachim GUDMUNDSSON , Benjamin HACHEY

Thesis abstract:

Standard named entity recognizers can effectively recognize entity mentions that consist of contiguous tokens and do not overlap with each other. However, in practice, there are many domains, such as the biomedical domain, in which there are nested, overlapping, and discontinuous entity mentions. These complex mentions cannot be directly recognized by conventional sequence tagging models because they may break the assumptions based on which sequence tagging techniques are built. We note that the drawbacks of existing methods can be broadly categorized into: (1) lack of expressivity; and (2) computational complexity. Our aim is to propose a model that recognizes all kinds of complex entity mentions, with low ambiguity level and low computational complexity.

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