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Most word embedding methods are proposed with general purpose which take a word as a basic unit and learn embeddings according to words' external contexts. However, in biomedical text mining, there are many biomedical entities and syntactic chunks which contain rich domain information, and the semantic meaning of a word is also strongly related to those information. Hence, we present a biomedical...
Biomedical named entity recognition (bio-NER), which extracts important entities such as genes and proteins, has become one of the most fundamental tasks in biomedical knowledge acquisition. However, the performance of traditional NER systems is always limited to the construction of complex hand-designed features which are derived from various linguistic analyses and maybe only adapted to specified...
Entity relation extraction is an important task for obtaining useful information from multiple text documents. This paper presents a distributed meta-learning method which incorporates the distributed system and the meta-learning strategy for Chinese entity relation extraction. At the basic level of the meta-learning, we construct a learner for each relation type and the basic learners are different...
Protein-Protein Interaction (PPI) extraction from literatures is becoming a more and more significant task in the biomedical information extraction. Though many methods for PPI extraction have achieved promising results, they all concentrated on the abstracts of literatures rather than full texts. In this paper, we append full-text features, namely Location and Co-occurrence to extract PPIs from full...
Coreference resolution recently plays a more and more important role for many natural language processing tasks. In this paper, we propose two methods for the biomedical coreference resolution. One is the single machine learning method (SVM ranker-learning algorithm) which selects appropriate features for the pronoun and noun phrase coreference resolution respectively. The other one is the hybrid...
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