Syntactic Parsing for Bio-molecular Event Detection from Scientific Literature

Transcription

1 Syntactic Parsing for Bio-molecular Event Detection from Scientific Literature Sérgio Matos 1, Anabela Barreiro 2, and José Luis Oliveira 1 1 IEETA, Universidade de Aveiro, Campus Universitário de Santiago, Aveiro, Portugal 2 Faculdade de Letras, Universidade do Porto, Via Panorâmica, Porto, Portugal {aleixomatos,jlo}@ua.pt, barreiro_anabela@hotmail.com Abstract. Rapid advances in science and in laboratorial and computing methods are generating vast amounts of data and scientific literature. In order to keep up-to-date with the expanding knowledge in their field of study, researchers are facing an increasing need for tools that help manage this information. In the genomics field, various databases have been created to save information in a formalized and easily accessible form. However, human curators are not capable of updating these databases at the same rate new studies are published. Advanced and robust text mining tools that automatically extract newly published information from scientific articles are required. This paper presents a methodology, based on syntactic parsing, for identification of gene events from the scientific literature. Evaluation of the proposed approach, based on the BioNLP shared task on event extraction, produced an average F-score of 47.1, for six event types. Keywords: Biomedical literature, information extraction, bio-molecular events, syntactic parsing, semantic properties. 1 Introduction Recent advances in biotechnology, namely the widespread use of high-throughput methods for gene analysis, have originated vast amounts of published scientific literature. While much of the data and results described in these studies are being annotated in the various existing biomedical databases, these are not easily kept up-to-date. As a result, many relevant research outcomes are still enclosed as free-text in the scientific literature, which remains the major source of information for researchers [1]. It is therefore increasingly difficult for researchers to keep track of the quickly expanding biomedical knowledge to support their experiment planning and analysis of results [2][3]. Researchers are currently faced with issues such as (i) how to identify the most relevant articles for their specific study, (ii) how to identify the mentioned concepts (genes, proteins, diseases and so on) and relations between them, and (iii) how to integrate the extracted information with the existing knowledge in a simple, efficient, and userfriendly manner [2][4]. This integrated view of information extracted from literature, in the framework of more systematized and formalized knowledge annotated in databases and ontologies, is an important requisite for biological data analysis [3]. L. Seabra Lopes et al. (Eds.): EPIA 2009, LNAI 5816, pp , Springer-Verlag Berlin Heidelberg 2009

2 80 S. Matos, A. Barreiro, and J.L. Oliveira To address these issues, several tools have been developed in the past years that combine Information Extraction (IE), Text Mining (TM) and Natural Language Processing (NLP) techniques with the domain knowledge available in resources such as the Entrez Gene, UniProt, GO or UMLS [1][2][4][5]. Such tools process text titles and abstracts from the MEDLINE/PubMed [6] literature database and present the extracted information in different forms. The ihop tool [7] identifies genes and proteins in PubMed abstracts and uses them as links, allowing the navigation through sentences and abstracts. The AliBaba system [8] is based on pattern matching and cooccurrence statistics to find associations between biological entities such as genes, proteins or diseases, and presents the search results in the form of a graph. EBIMed [9] also finds associations between protein/gene names, GO annotations, drugs and species in PubMed abstracts resulting from a user query. The results are displayed in a table with links to the sentences and abstracts that support the corresponding associations. A similar tool, FACTA [10] retrieves abstracts from PubMed and identifies biomedical concepts (e.g. genes/proteins, diseases, enzymes and chemical compounds) co-occurring with the user query term. The concepts are presented to the user in a tabular format and ranked based on the co-occurrence statistics or on pointwise mutual information. More recently, there has been some focus on applying more detailed linguistic processing in order to improve information retrieval and extraction. Chilibot [11] retrieves sentences from PubMed abstracts related to a pair or a list of proteins, genes, or keywords, and applies shallow parsing to classify these sentences as interactive, non-interactive or simple abstract co-occurrence. The identified relationships between entities or keywords are then displayed as a graph. MEDIE [12] uses a deep-parser and a term recognizer to index abstracts based on pre-computed semantic annotations, allowing for real-time retrieval of sentences containing biological concepts associated with the terms specified in the user query. Interest in the application of more advanced methods of linguistic processing is also evident in the recent information extraction evaluation challenges, namely the BioNLP shared task on event extraction [13] and the BioCreAtIvE II.5 challenge [14], which investigate the extraction of gene events from literature. In this paper, we describe a methodology based on syntactic parsing to detect and annotate bio-molecular events, such as protein production and breakdown, localization or binding events. We present results from our participation in the BioNLP shared task and discuss the main difficulties and further developments required in this area. 2 Methods The method described in this paper to identify bio-molecular events is based on syntactic grammars that process texts and detect the occurrence of linguistic patterns that describe such events. Syntactic parsing was implemented using NooJ [15], a freely available development environment and linguistic processing engine that includes tools for inflectional and derivational morphology, syntactic grammars and semantics. NooJ uses dictionaries and grammars to produce formalized descriptions of natural language and contains a system of inflectional and derivational paradigms, which interacts with the dictionary. Inflectional rules apply to a dictionary entry (lemma) to recognize and generate inflected forms, including gender, number and tense. Derivational

3 Syntactic Parsing for Bio-molecular Event Detection from Scientific Literature 81 rules apply to a dictionary entry to recognize and generate derived forms, such as nominalizations (predicate nouns morphosyntactically related to a verb) as adopted in [16]. Lemmas can also have semantic information included. Semantic properties allow, for example, adding the characteristic of a particular named entity, such as ORGANISM, PROTEIN or DISEASE. These properties are illustrated in Table 1. Table 1. Dictionary entries in NooJ Lemma PoS FLX Semantic properties ID TAXID human N TABLE ORGANISM 9606 Homo sapiens N ORGANISM 9606 Breast cancer type 1 N PROTEIN P susceptibility protein BRCA1 N PROTEIN P BRCA1 N PROTEIN P BRCA1 N GENE RNF53 N GENE To create the dictionaries used in this method, we adapted the verb dictionary from the biomedical resource BioLexicon [17][18]. BioLexicon includes verbs that occur frequently in the biomedical literature and that usually describe a specific event, such as express, bind and transcribe. We enhanced the BioLexicon dictionary with inflectional ( FLX ) and derivational ( DRV ) attributes and with semantic properties, as shown in Table 2. For example, ION:TABLE represents the derivational and inflectional paradigms for the nominalization expression (which inflects as the word TA- BLE), and ABOLISH represents the inflectional paradigm for the verb express. The semantic properties in NooJ dictionaries were used to assign specific event types to the verbs in the literature that describe those events. In Table 2, the verb stimulate, for example, is assigned a semantic property EventType with a value Positive_Regulation. This semantic property is then used in the syntactic grammars, which add an annotation to that type of event whenever it is detected in texts. Table 2. Definition of verbs in the dictionary Lemma PoS DRV FLX EventType express V ION:TABLE ABOLISH Gene_expression ligate V TION:TABLE SMILE Binding stimulate V TION:TABLE SMILE Positive_regulation The inflectional and derivational paradigms are described in terms of re-write rules. For example, the noun inflectional paradigm TABLE, defines that the plural of the dictionary word associated with this rule is formed by adding an s to the lemma. Hence, the plural of any word associated with the attribute +FLX=TABLE (ex. human ) will be obtained in the same way. In the case of verbs, inflectional rules describe the conjugation of the verb. For example, the inflectional paradigm SMILE defines re-write rules in terms of person, number and tense for verbs that

4 82 S. Matos, A. Barreiro, and J.L. Oliveira conjugate like the verb to smile. Similarly, the derivational system allows the derivation of a word, as defined by the derivational rule. This allows, for example, obtaining nouns and adjectives from verb entries. The derived word maintains the semantic properties of the word from which is derived (lemma). Thus, the predicate noun stimulation is produced and linked to a positive regulation event, through its inherited semantic properties from the verb stimulate. In order to define the type of events linked to each verb, we used the training data in the BioNLP shared task. Based on the manual linguistic annotations, we extracted the sentences corresponding to each event, and assigned the event type to the verbs found on those sentences. We then manually checked this list and selected only those verbs showing a specific link to a type of event. In case verbs were linked to more than one event type, only the most frequent event type was selected, and the remaining ones removed. In NooJ, syntactic grammars can be used to process sequences of tokens to recognize and annotate multiword expressions. In the approach used, our aim was to detect linguistic patterns, based on named entities (genes and proteins) and on biologically relevant verbs and verb nominalizations referencing some type of bio-molecular event. These entities, verbs and nouns are automatically annotated by NooJ when the dictionaries and grammars are applied to texts. In order to create the relevant grammars, we first used NooJ to extract general concordances from the texts that included an annotated gene or protein and a verb or nominalization. We then identified, in the examples provided by the concordances, specific grammatical constructions describing different types of events. For example, we were able to identify a simple pattern composed of a nominalization, the particle of and a named gene or protein, as in expression of p53 or stimulation of CD4. These patterns were described in terms of syntactic grammars, as illustrated in Fig. 1. The output of the grammar (shown below the connecting lines) identifies the protein ( CD4 ), the expression referencing the event ( stimulation ) and the type of event. Construction and refinement of the syntactic grammars is an iterative process. After creating a baseline grammar to describe a particular construction, we try to incorporate syntactic-semantic variants (paraphrases) in order to achieve better recall, without compromising precision. For example, the grammar used to identify the construction expression of p53 should also be able to identify expression of gene p53 or expression of the human gene p53. The training and development data sets of the shared task were used during this iterative process. The semantic properties included in the dictionary are used in the syntactic grammars to specify the event type in the annotation. Example 1 shows the output of the grammar in Fig. 1: CD4 is the named entity and stimulation is the expression identifying the bio-molecular event. The event type, positive regulation, is obtained directly from the expression s semantic properties. Example 1. Grammar output used to annotate the expression in texts Stimulation of human CD4 <EVENT+PROTEIN=CD4+EXP=Stimulation+TYPE=Positive_regula tion>

5 Syntactic Parsing for Bio-molecular Event Detection from Scientific Literature 83 Fig. 1. Grammar to detect phrases, such as stimulation of CD4 3 Results The application of the grammars described in the previous section allowed the extraction of phrases that reference gene related events. Table 3 shows some examples of the patterns described by these grammars and the corresponding concordances found in texts. Although these are relatively simple patterns, they can model a large portion of the language used to present such events. Table 3. Patterns detected by the grammars Pattern Concordance in text <entity> [<entity_type>] <nominalization> HSP gene expression <nominalization> of [<entity_type>] <entity> upregulation of Fas <entity> [<entity_type>] <be> [ not ] [<adverb>] <verb> IL-2R stimulation was totally inhibited <verb> <preposition> <entity> binding of TRAF2 <verb> <nominalization> of <entity> suppressing activation of STAT6 This section presents the evaluation results of the proposed method, obtained using the test data from the BioNLP shared task on event extraction. This data set was not used for defining the semantic properties to include in the dictionary or for creating the syntactic grammars. The aim of the shared task was to detect gene events in Pub- Med abstracts and create the corresponding annotations, including the protein(s) involved, the referencing expression or trigger and the type of event. The data for the BioNLP task was derived from the GENIA event corpus and comprised 800 abstracts in the training set, 150 in the development set, and 260 in the test set. Details on the annotation procedure and evaluation metrics are described in [13]. The BioNLP shared task divided events into nine types. The regulatory events were not included in this study due to time constraints and to the more complex structure of those events. Results for the remaining six event types are displayed in Table 4. These results were achieved using six grammars similar to the one exemplified in Fig. 1. An average F-score of was obtained. Except for binding events, the results are promising and show that a good performance can be obtained using this simple approach. In

6 84 S. Matos, A. Barreiro, and J.L. Oliveira Table 4. Performance of the event detection method (test data) Event type Recall Precision F-score Localization Binding Gene Expression Transcription Protein Catabolism Phosphorylation Average the case of binding events, the participation of two proteins creates extra difficulty in describing such events, and the results are still poor. 4 Discussion We have described an approach which uses syntactic grammars to detect and annotate gene events from the scientific literature. The proposed method takes advantage of the inflectional and derivational morphology and the semantic properties established in dictionaries and grammars developed with NooJ, which allow to associate terminological verbs and their derivations to specific event types. This approach provides a general and flexible solution for information extraction from biomedical texts. The results illustrated in Table 4 indicate that this approach can be used to process the literature and extract networks of events and interactions. These networks are valuable for literature search and navigation, as proposed in MEDIE or Chilibot tools, but require much less processing. However, some shortcomings need to be considered and improved. The first limitation is related to named entity recognition. In the BioNLP shared task, participants were supplied with the names and positions in text of mentioned genes and proteins. In such a setup, recognizing linguistic patterns where these entities occur is significantly simplified. In a more realistic task, the processing pipeline would not have the list of mentioned entities as an input and a named entity recognizer with a very good performance needs to be included in the processing steps. Another limitation concerns the identification of patterns and creation of grammars. Although a manual procedure such as the one taken can identify the most salient linguistic patterns, it would be interesting to investigate the possibility to generate and assess new patterns automatically. In this study, we have not included the gene regulatory events because these are frequently referenced by more complex constructions which are not yet covered by our grammars. Describing and extracting these events is of great importance and will become a future direction of our work. Finally, it is important to assess the advantages and disadvantages of the proposed approach for identifying relations and events, when compared to other methods based on shallow or deep parsing. Methods such as the one proposed in this paper can be used to help database curators identify the most relevant facts in the literature and speed-up the annotation process. Tools based on these methods can also provide alternative querying and browsing of facts cited in the literature and be useful for researchers. However, before these

7 Syntactic Parsing for Bio-molecular Event Detection from Scientific Literature 85 methods can be truly useful, they must be included in user-oriented tools that offer robust and reliable performance while hiding the complexity of the linguistic processing. It is also of major importance that these tools keep links to the reference databases so that users can navigate from the literature to these resources and back, in a simple and fluid way. References 1. Rebholz-Schuhmann, D., Kirsch, H., Couto, F.: Facts from text: is text mining ready to deliver? PLoS Biol. 3, e65 (2005) 2. Altman, R.B., Bergman, C.M., Blake, J., Blaschke, C., Cohen, A., Gannon, F., Grivell, L., Hahn, U., Hersh, W., Hirschman, L., Jensen, L.J., Krallinger, M., Mons, B., O Donoghue, S.I., Peitsch, M.C., Rebholz-Schuhmann, D., Shatkay, H., Valencia, A.: Text mining for biology - the way forward: opinions from leading scientists. Genome Biology 9(suppl. 2), S7 (2008) 3. Jensen, L.J., Saric, J., Bork, P.: Literature mining for the biologist: from information retrieval to biological discovery. Nat. Rev. Genet. 7, (2006) 4. Shatkay, H.: Hairpins in bookstacks: Information retrieval from biomedical text. Briefings in Bioinformatics 6(3), (2005) 5. Weeber, M., Kors, J.A., Mons, B.: Online tools to support literature-based discovery in the life sciences. Briefings in Bioinformatics 6(3), (2005) 6. PubMed, 7. Hoffmann, R., Valencia, A.: ihop - A Gene Network for Navigating the Literature. Nature Genetics 36, 664 (2004) 8. Plake, C., Schiemann, T., Pankalla, M., Hakenberg, J., Leser, U.: Ali Baba: PubMed as a graph. Bioinformatics 22(19), (2006) 9. Rebholz-Schuhmann, D., Kirsch, H., Arregui, M., Gaudan, S., Riethoven, M., Stoehr, P.: EBIMed text crunching to gather facts for proteins from Medline. Bioinformatics 23(2), (2007) 10. Tsuruoka, Y., Tsujii, J., Ananiadou, S.: FACTA: a text search engine for finding associated biomedical concepts. Bioinformatics 24(21), (2008) 11. Chen, H., Sharp, B.M.: Content-rich biological network constructed by mining PubMed abstracts. BMC Bioinformatics 5, 147 (2008) 12. Miyao, Y., Ohta, T., Masuda, K., Tsuruoka, Y., Yoshida, K., Ninomiya, T., Tsujii, J.: Semantic Retrieval for the Accurate Identification of Relational Concepts in Massive Textbases. In: Proceedings of COLING-ACL 2006, Sydney, pp (2006) 13. Kim, J.-D., Ohta, T., Pyysalo, S., Kano, Y., Tsujii, J.: Overview of BioNLP 2009 Shared Task on Event Extraction. In: Proceedings of Natural Language Processing in Biomedicine (BioNLP) NAACL 2009 Workshop (2009) 14. BioCreAtIvE - Critical Assessment of Information Extraction Systems in Biology, NooJ, Barreiro, A.M.: Make it simple with paraphrases: Automated paraphrasing for authoring aids and machine translation. PhD dissertation. Faculdade de Letras da Universidade do Porto, Porto (2008) 17. Sasaki, Y., Montemagni, S., Pezik, P., Rebholz-Schuhmann, D., McNaught, J., Ananiadou, S.: BioLexicon: A Lexical Resource for the Biology Domain. In: Proceedings of the Third International Symposium on Semantic Mining in Biomedicine (2008) 18. BOOTStrep Bio-Lexicon,