We introduce our incremental coreference resolution system for the BioNLP 2011 Shared Task on Protein/Gene Znteraction. The beneﬁts of an incremental architecture over a mentionpair model are: a reduction of the number of candidate pairs, a means to overcome the problem of underspeciﬁed items in pair-wise classiﬁcation and the natural integration of global constraints such as transitivity. A ﬁltering system takes into account speciﬁc features of different anaphora types. We do not apply Machine Learning, instead the system classiﬁes with an empirically derived salience measure based on the dependency labels of the true mentions. The OntoGene pipeline is used for preprocessing

Clematide, S

Klenner, M

Rinaldi, Fabio

Schneider, G

Tuggener, D

ZORA

An Incremental Model for the Coreference Resolution Task of BioNLP 2011Don Tuggener, Manfred Klenner, Gerold Schneider, Simon Clematide, Fabio RinaldiInstitute of Computational Linguistics, University of Zurich, Switzerland{tuggener,klenner,gschneid,siclemat,rinaldi}@cl.uzh.chAbstractWe introduce our incremental coreference res-olution system for the BioNLP 2011 SharedTask on Protein/Gene interaction. The benefitsof an incremental architecture over a mention-pair model are: a reduction of the numberof candidate pairs, a means to overcome theproblem of underspecified items in pair-wiseclassification and the natural integration ofglobal constraints such as transitivity. A fil-tering system takes into account specific fea-tures of different anaphora types. We do notapply Machine Learning, instead the systemclassifies with an empirically derived saliencemeasure based on the dependency labels of thetrue mentions. The OntoGene pipeline is usedfor preprocessing.1 IntroductionThe Coreference Resolution task of BioNLP fo-cused on finding anaphoric references to proteinsand genes. Only antecedent-anaphora pairs are con-sidered in evaluation and not full coreference sets.Although it might not seem to be necessary to gen-erate full coreference sets, anaphora resolution stillbenefits from their establishment. Our incrementalapproach (Klenner et al., 2010) naturally enforcestransitivity constraints and thereby reduces the num-ber of potential antecedent candidates. The systemachieved good results in the BioNLP 2011 sharedtask (Fig. 1)Team R P F1A 22.18 73.26 34.05Our model 21.48 55.45 30.96B 19.37 63.22 29.65C 14.44 67.21 23.77D 3.17 3.47 3.31E 0.70 0.25 0.37Figure 1: Protein/Gene Coreference Task2 Preprocessing: The OntoGene PipelineOntoGene’s text mining system is based on aninternally-developed fast, broad-coverage, deep-syntactic parsing system (Schneider, 2008). Theparser is wrapped into a pipeline which uses a num-ber of other NLP tools. The parser is a key compo-nent in a pipeline of NLP tools (Rinaldi et al., 2010),used to process input documents. First, in a pre-processing stage, the input text is transformed intoa custom XML format, and sentences and tokensboundaries are identified. The OntoGene pipelinealso includes a step of term annotation and disam-biguation, which are not used for the BioNLP sharedtask, since relevant terms are already provided inboth the training and test corpora. The pipeline alsoincludes part-of-speech taggers, a lemmatizer and asyntactic chunker.When the pipeline finishes, each input sentencehas been annotated with additional information,which can be briefly summarized as follows: sen-tences are tokenized and their borders are detected;each sentence and each token has been assigned anID; each token is lemmatized; tokens which be-long to terms are grouped; each term is assigned anormal-form and a semantic type; tokens and termsare then grouped into chunks; each chunk has atype (NP or VP) and a head token; each sentenceis described as a syntactic dependency structure. Allthis information is represented as a set of predicatesand stored into the Knowledge Base of the system,which can then be used by different applications,such as the OntoGene Relation Miner (Rinaldi et al.,2006) and the OntoGene Protein-Protein Interactiondiscovery tool (Rinaldi et al., 2008).3 Our Incremental Model for CoreferenceResolution1 for i=1 to length(I)2 for j=1 to length(C)3 rj := virtual prototype of coreference set Cj4 Cand := Cand ⊕ rj if compatible(rj ,mi)5 for k= length(B) to 16 bk:= the k-th licensed buffer element7 Cand := Cand ⊕ bk if compatible(bk,mi)8 if Cand = {} then B := B ⊕mi9 if Cand 6= {} then10 antei := most salient element of Cand11 C := augment(C,antei,mi)Figure 2: Incremental model: base algorithmFig. 2 shows the base algorithm. Let I be thechronologically ordered list of NPs, C be the setof coreference sets and B a buffer, where NPs arestored, if they are not anaphoric (but might be validantecedents). Furthermore mi is the current NP and⊕ means concatenation of a list and a single item.The algorithm proceeds as follows: a set of an-tecedent candidates is determined for each NP mi(steps 1 to 7) from the coreference sets (rj) and thebuffer (bk). A valid candidate rj or bk must be com-patible with mi. The definition of compatibility de-pends on the POS tags of the anaphor-antecedentpair. The most salient available candidate is selectedas antecedent for mi.3.1 Restricted Accessibility of AntecedentCandidatesIn order to reduce underspecification, mi is com-pared to a virtual prototype of each coreference set(similar to e.g. (Luo et al., 2004; Yang et al., 2004;Rahman and Ng, 2009)). The virtual prototype bearsmorphologic and semantic information accumulatedfrom all elements of the coreference set. Access tocoreference sets is restricted to the virtual prototype.This reduces the number of considered pairs (fromthe cardinality of a set to 1).3.2 Filtering based on Anaphora TypePotentionally co-refering NPs are extracted from theOntoGene pipeline based on POS tags. We then ap-ply filtering based on anaphora type: Reflexive pro-nouns must be bound to a NP that is governed by thesame verb. Relative pronouns are bound to the clos-est NP in the left context. Personal and possessivepronouns are licensed to bind to morphologicallycompatible antecedent candidates within a windowof two sentences. Demonstrative NPs containing thelemmata ’protein’ or ’gene’ are licensed to bind toname containing mentions. Demonstrative NPs notcontaining the trigger lemmata can be resolved tostring matching NPs preceding them1.3.3 Binding Theory as a FilterWe know through binding theory that ’modulator’and ’it’ cannot be coreferent in the sentence ”Over-expression of protein inhibited stimulus-mediatedtranscription, whereas modulator enhanced it”.Thus, the pair ’modulator’-’it’ need not be consid-ered at all. We have not yet implemented a full-1As we do not perform anaphoricity determination of nom-inal NPs, we do not consider bridging anaphora (anaphoricnouns that are connected to their antecedents through seman-tic relations and cannot be identified by string matching).blown binding theory. Instead, we check if the an-tecedent and the anaphor are governed by the sameverb.4 An Empirically-based Salience MeasureOur salience measure is a partial adaption of themeasure from (Lappin and Leass, 1994). Thesalience of a NP is solely defined by the salienceof the dependency label it bears. The salience of adependency label, D, is estimated by the number oftrue mentions (i.e. co-refering NPs) that bear D (i.e.are connected to their heads with D), divided by thetotal number of true mentions (bearing any D). Thesalience of the label subject is thus calculated by:Number of truementions bearing subjectTotal number of truementionsWe get a hierarchical ordering of the dependency la-bels (subject > object > pobject > ...) according towhich antecedents are ranked and selected.ReferencesManfred Klenner, Don Tuggener, and Angela Fahrni. 2010. Inkre-mentelle koreferenzanalyse fu¨r das deutsche. In Proceedings der10. Konferenz zur Verarbeitung Natu¨rlicher Sprache.Shalom Lappin and Herbert J Leass. 1994. An algorithm for pronomi-nal anaphora resolution. Computational Linguistics, 20:P. 535–561.Xiaoqiang Luo, Abe Ittycheriah, Hongyan Jing, Nanda Kambhatla, andSalim Roukos. 2004. A mention-synchronous coreference resolu-tion algorithm based on the bell tree. In Proceedings of the 42ndAnnual Meeting on Association for Computational Linguistics.Altaf Rahman and Vincent Ng. 2009. Supervised models for corefer-ence resolution. In Proceedings of the 2009 Conference on Empir-ical Methods in Natural Language Processing: Volume 2 - Volume2, EMNLP ’09, pages 968–977, Stroudsburg, PA, USA. Associationfor Computational Linguistics.Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand, Michael Hess, andMartin Romacker. 2006. An Environment for Relation Mining overRichly Annotated Corpora: the case of GENIA. BMC Bioinformat-ics, 7(Suppl 3):S3.Fabio Rinaldi, Thomas Kappeler, Kaarel Kaljurand, Gerold Schnei-der, Manfred Klenner, Simon Clematide, Michael Hess, Jean-Marcvon Allmen, Pierre Parisot, Martin Romacker, and Therese Vachon.2008. OntoGene in BioCreative II. Genome Biology, 9(Suppl2):S13.Fabio Rinaldi, Gerold Schneider, Kaarel Kaljurand, Simon Clematide,Therese Vachon, and Martin Romacker. 2010. OntoGene inBioCreative II.5. IEEE/ACM Transactions on Computational Bi-ology and Bioinformatics, 7(3):472–480.Gerold Schneider. 2008. Hybrid Long-Distance Functional Depen-dency Parsing. Doctoral Thesis, Institute of Computational Linguis-tics, University of Zurich.Xiaofeng Yang, Jian Su, Guodong Zhou, and Chew Lim Tan. 2004. Annp-cluster based approach to coreference resolution. In Proceedingsof the 20th international conference on Computational Linguistics.

An incremental model for the coreference resolution task of BioNLP 2011

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An incremental model for the coreference resolution task of BioNLP 2011

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