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BIO‐MOLECULAR EVENT EXTRACTION WITH MARKOV LOGIC
Author(s) -
Riedel Sebastian,
Sætre Rune,
Chun HongWoo,
Takagi Toshihisa,
Tsujii Jun’ichi
Publication year - 2011
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2011.00400.x
Subject(s) - computer science , event (particle physics) , artificial intelligence , inference , task (project management) , margin (machine learning) , sentence , probabilistic logic , pipeline (software) , markov chain , machine learning , natural language processing , relationship extraction , markov model , hidden markov model , information extraction , physics , management , quantum mechanics , economics , programming language
This article presents a novel approach to event extraction from biological text using Markov Logic. It can be described by three design decisions: (1) instead of building a pipeline using local classifiers, we design and learn a joint probabilistic model over events in a sentence; (2) instead of developing specific inference and learning algorithms for our joint model, we apply Markov Logic, a general purpose Statistical Relation Learning language, for this task; (3) we represent events as relations over the token indices of a sentence, as opposed to structures that relate event entities to gene or protein mentions. In this article, we extend our original work by providing an error analysis for binding events. Moreover, we investigate the impact of different loss functions to precision, recall and F‐measure. Finally, we show how to extract events of different types that share the same event clue. This extension allowed us to improve our performance our performance even further, leading to the third best scores for task 1 (in close range to the second place) and the best results for task 2 with a 14% point margin.