We review recent results in literature data mining for biology and discuss the need and the steps for a challenge evaluation for this field. Literature data mining has progressed from simple recognition of terms to extraction of interaction relationships from complex sentences, and has broadened from recognition of protein interactions to arange of problems such as improving homology search, identifying cellular location, and so on. To encourage participation and accelerate progress in this expanding field, we propose creating challenge evaluations, and we describe two specific applications in this context.

We have designed and implemented an information extraction system using a full parser to investigate the plausibility of full analysis of text using general-purpose parser and grammar applied to biomedical domain. We partially solved the problems of full parsing of inefficiency, ambiguity, and low coverage by introducing the preprocessors, and proposed the use of modules that handles partial results of parsing for further improvement. Our approach makes it possible to modularize the system, so that the IEsystem as a whole becomes easy to be tuned to specific domains, and easy to be maintained and improved by incorporating various techniques of disambiguation, speed up, etc. In preliminary experiment, from 133 argument structures that should be extracted from 97 sentences, we obtained 23% uniquely and 24 % with ambiguity. And 20 % are extractable from not complete but partial results of full parsing. 1

Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data relevant to genes of the human genome from the 11 million abstracts in Medline. However, extraction eorts have been frustrated by the lack of conventions for describing human genes and proteins. We have developed and evaluated a variety of learned information extraction systems for identifying human protein names in Medline abstracts and subsequently extracting information on interactions between the proteins. We demonstrate that machine learning approaches using support vector machines and maximum entropy are able to identify human proteins with higher accuracy than several previous approaches. We also demonstrate that various rule induction methods are able to identify protein interactions with higher precision than manually-developed rules.

With the information overload in genome-related field, there is an infreest need for natural language processing technology to extract information from literature and various attempts of information extraction using NLP has been being made. We are developing the necessary resources including domain ontology and annotated corpus from research abstracts in MEDLINE database (GENIA corpus). We are building the ontology and the corpus simultaneously, using each other. In this paper we report on our new corpus, its ontological basis, annotation scheme, and statistics of annotated objects. We also describe the tools used for corpus annotation and management. 1.

Introduction The application of methods from the eld of natural language processing to biological data has long been restricted to the parsing of molecular structures such as DNA ### . More recently, however, efforts have also been directed to capturing content from biological documents (research reports, journal articles, etc.), either dealing with restricted information extraction problems such as name recognition for proteins or gene products ##### ,ormore sophisticated ones which aim at the acquisition of knowledge relating to protein or enzyme interactions, molecular binding behavior, etc. ####### . Current information extraction (IE) systems, however, suffer from various weaknesses. First, their range of understanding is bounded by rather limited domain knowledge. The templates these systems are supplied with allow only factual information about particular, a priori chosen entities (cell type, virus type, protein group, etc.) to be assembled from the analyzed documents.

The immense growth in the volume of research literature and experimental data in the field of molecular biology calls for e#cient automatic methods to capture and store information. In recent years, several groups have worked on specific problems in this area, such as automated selection of articles pertinent to molecular biology, or automated extraction of information using natural-language processing, information visualization, and generation of specialized knowledge bases for molecular biology. GeneWays is an integrated system that combines several such subtasks. It analyzes interactions between molecular substances, drawing on multiple sources of information to infer a consensus view of molecular networks. GeneWays is designed as an open platform, allowing researchers to query, review, and critique stored information.

Motivation: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge discovery that can help biologists to gather and make use of the knowledge encoded in text documents. In order to make organized and structured information available, automatically recognizing biomedical entity names becomes critical and is important for information retrieval, information extraction and automated knowledge acquisition. Results: In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena of naming conventions in the biomedical domain, we

A prerequisite for all higher level information extraction tasks is the identification of unknown names in text. Today, when large corpora can consist of billions of words, it is of utmost importance to develop accurate techniques for the automatic detection, extraction and categorization of named entities in these corpora. Although named entity recognition might be regarded a solved problem in some domains, it still poses a signi cant challenge in others. In this work we focus on one of the more difficult tasks, the identification of protein names in text.

A complex relation is any n-ary relation in which some of the arguments may be be unspecified. We present here a simple two-stage method for extracting complex relations between named entities in text. The first stage creates a graph from pairs of entities that are likely to be related, and the second stage scores maximal cliques in that graph as potential complex relation instances. We evaluate the new method against a standard baseline for extracting genomic variation relations from biomedical text. 1

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