Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature

Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature Claudio Giuliano and Alberto Lavelli and Lorenza Romano ITC-irst Via Sommarive, 18 38050, Povo (TN) Italy {giuliano,lavelli,romano}@itc.it Abstract We propose an approach for extracting re- lations between entities from biomedical literature based solely on shallow linguis- tic information. We use a combination of kernel functions to integrate two different information sources: (i) the whole sen- tence where the relation appears, and (ii) the local contexts around the interacting entities. We performed experiments on ex- tracting gene and protein interactions from two different data sets. The results show that our approach outperforms most of the previous methods based on syntactic and semantic information. 1 Introduction Information Extraction (IE) is the process of find- ing relevant entities and their relationships within textual documents. Applications of IE range from Semantic Web to Bioinformatics. For example, there is an increasing interest in automatically extracting relevant information from biomedi- cal literature. Recent evaluation campaigns on bio-entity recognition, such as BioCreAtIvE and JNLPBA 2004 shared task, have shown that sev- eral systems are able to achieve good performance (even if it is a bit worse than that reported on news articles). However, relation identification is more useful from an applicative perspective but it is still a considerable challenge for automatic tools. In this work, we propose a supervised machine learning approach to relation extraction which is applicable even when (deep) linguistic process- ing is not available or reliable. In particular, we explore a kernel-based approach based solely on shallow linguistic processing, such as tokeniza- tion, sentence splitting, Part-of-Speech (PoS) tag- ging and lemmatization. Kernel methods (Shawe-Taylor and Cristianini, 2004) show their full potential when an explicit computation of the feature map becomes compu- tationally infeasible, due to the high or even infi- nite dimension of the feature space. For this rea- son, kernels have been recently used to develop innovative approaches to relation extraction based on syntactic information, in which the examples preserve their original representations (i.e. parse trees) and are compared by the kernel function (Zelenko et al., 2003; Culotta and Sorensen, 2004; Zhao and Grishman, 2005). Despite the positive results obtained exploiting syntactic information, we claim that there is still room for improvement relying exclusively on shal- low linguistic information for two main reasons. First of all, previous comparative evaluations put more stress on the deep linguistic approaches and did not put as much effort on developing effec- tive methods based on shallow linguistic informa- tion. A second reason concerns the fact that syn- tactic parsing is not always robust enough to deal with real-world sentences. This may prevent ap- proaches based on syntactic features from produc- ing any result. Another related issue concerns the fact that parsers are available only for few lan- guages and may not produce reliable results when used on domain specific texts (as is the case of the biomedical literature). For example, most of the participants at the Learning Language in Logic (LLL) challenge on Genic Interaction Extraction (see …

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