We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information . We use a combination of kernel functions to integrate two different information sources : ( i ) the whole sentence where the relation appears , and ( ii ) the local contexts around the interacting entities . We performed experiments on extracting 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 . Information Extraction ( IE ) is the process of finding 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 biomedical literature . Recent evaluation campaigns on bio-entity recognition , such as BioCreAtIvE and JNLPBA 2004 shared task , have shown that several 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 processing 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 ) tagging and lemmatization . Kernel methods ( Shawe-Taylor and Cristianini , 2004 ) show their full potential when an explicit computation of the feature map becomes computationally infeasible , due to the high or even infinite dimension of the feature space . For this reason , 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 shallow 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 effective methods based on shallow linguistic information . A second reason concerns the fact that syntactic parsing is not always robust enough to deal with real-world sentences . This may prevent approaches based on syntactic features from producing any result . Another related issue concerns the fact that parsers are available only for few languages 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 Section 4.2 ) were unable to successfully exploit linguistic information provided by parsers . It is still an open issue whether the use of domainspecific treebanks ( such as the Genia treebank 1 ) can be successfully exploited to overcome this problem . Therefore it is essential to better investigate the potential of approaches based exclusively on simple linguistic features . In our approach we use a combination of kernel functions to represent two distinct information sources : the global context where entities appear and their local contexts . The whole sentence where the entities appear ( global context ) is used to discover the presence of a relation between two entities , similarly to what was done by Bunescu and Mooney ( 2005b ) . Windows of limited size around the entities ( local contexts ) provide useful clues to identify the roles of the entities within a relation . The approach has some resemblance with what was proposed by Roth and Yih ( 2002 ) . The main difference is that we perform the extraction task in a single step via a combined kernel , while they used two separate classifiers to identify entities and relations and their output is later combined with a probabilistic global inference . We evaluated our relation extraction algorithm on two biomedical data sets ( i.e. the AImed corpus and the LLL challenge data set ; see Section 4 ) . The motivations for using these benchmarks derive from the increasing applicative interest in tools able to extract relations between relevant entities in biomedical texts and , consequently , from the growing availability of annotated data sets . The experiments show clearly that our approach consistently improves previous results . Surprisingly , it outperforms most of the systems based on syntactic or semantic information , even when this information is manually annotated ( i.e. the LLL challenge ) . The good results obtained using only shallow linguistic features provide a higher baseline against which it is possible to measure improvements obtained using methods based on deep linguistic processing . In the near future , we plan to extend our work in several ways . First , we would like to evaluate the contribution of syntactic information to relation extraction from biomedical literature . With this aim , we will integrate the output of a parser ( possibly trained on a domain-specific resource such the Genia Treebank ) . Second , we plan to test the portability of our model on ACE and MUC data sets . Third , we would like to use a named entity recognizer instead of assuming that entities are already extracted or given by a dictionary . Our long term goal is to populate databases and ontologies by extracting information from large text collections such as Medline .
Deep linguistic features obtained by parsers are not always robust and available for limited languages and domains, however; applications of shallow features are under investigated.
They propose an approach for entity relation extraction using shallow linguistic information such as tokenization, sentence splitting, Part-of-Speech tagging and lemmatization coupled with kernel functions.
Evaluations of two biomedical datasets show that the proposed method outperforms existing systems which depend on syntactic or manually annotated semantic information. 