A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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display nominal and pronominal coreference chains in a user-friendly and easy-tounderstand manner, so that coreferences can be tagged or corrected with a simple click of the mouse. We will annotate, at coreference level, the Reference Corpus for the Processing of Basque (EPEC), a 300,000 word collection of written standard Basque that has been automatically tagged at different levels (morphology, surface syntax, and phrases). We will endeavor to solve nominal coreferences by combining rule-based techniques and machine learning approaches to pronominal anaphora resolution. The machine learning techniques will allow us to make use of existing resources for Basque, while using rule-based techniques for nominal coreference resolution will allow us to partially circumvent the problem of the limits of those resources. Our machine learner is trained on a part of the Eus3LB Corpus 2 [15], a collection of previously parsed journalistic texts. This corpus, the basis of the EPEC corpus, has been manually tagged at coreference level, but only pronominal anaphora are annotated [1]. The paper is organized as follows. Section 2 describes some of the most significant work on coreferences, followed by a section presenting the tools we use to annotate corpora automatically and our aim in carrying out this work. In section 4 we describe the automatic coreference resolution process, which is divided into two parts: nominal coreference resolution process and pronominal anaphora resolution process. Section 5 then presents our experimental results and section 6 closes the paper with some concluding remarks. 2 Related Work Recent work on coreference resolution has been largely dominated by machine learning approaches. In the SemEval-2010 task on Coreference Resolution in Multiple Languages 3 [24], most of the systems were based on those techniques [7, 26, 12]. Nevertheless, rule-based systems have also been applied successfully: in the CoNLL-2011 Shared Task, for example, the best result was obtained by [13], which proposes a coreference resolution system that is an incremental extension of the multi-pass sieve system proposed in [23]. This system is shifting from the supervised learning setting to an unsupervised setting. At the same time, most other systems proposed at CoNLL-2011 [8, 6, 10] were based on machine learning techniques. The advantage of these approaches is that there are many open-source platforms for machine learning and machine learning based coreference such as BART 4 [27] or the Illinois Coreference Package [5]. The state of the art for languages other than English varies considerably. A rule-based system for anaphora resolution in Czech is proposed in [14], which uses Treebank data containing more than 45,000 coreference links in almost 50,000 2 Eus3LB is part of the 3LB project. 3 http://stel.ub.edu/semeval2010-coref/systems 4 http://www.bart-coref.org/ 116
manually annotated Czech sentences. Most recently, a substantial amount of newly annotated data for Czech has prompted the application of a supervised machine learning approach to resolving noun phrase coreferences in Czech [20]. On the other hand, [16] presents an approach to Persian pronoun resolution based on machine learning techniques. Other authors present an end-to-end coreference resolution rule-based system for Polish [21]. 3 The Tagging Process The annotation of coreference in Basque starts out with an annotated corpus that provides us with an easier work environment, one that focuses on the specific structures that could be part of a reference chain. The EPEC corpus has been morphosyntactically analyzed by means of MORFEUS [2]. After that, two automatic taggers (rule-based and stochastic) disambiguate at the lemmatization level. Finally, entities, chunks and complex postpositions are identified by means of the following tools: i) EIHERA, which identifies entities (Institution, Person and Location) [3]; and ii) IXATI Chunker [11], which identifies verb chains, noun phrase units, and complex postpositions. Referring to the annotation of pronominal anaphora, 25.000 words of this corpus was carried out manually. For this tagging, we used the MMAX2 application [19] (adapted to the established requirements). Although the annotation tool is adequate, the process is still arduous and time consuming; we wanted to make it faster and more user-friendly. Toward this end, we developed an automatic coreference resolution system and transported the results it produced into the MMAX2 output window. Thus, depending on the type of coreference, the tool now displays either the possible chains or the possible antecedents. Coreference mentions appear highlighted in the text, so that simply by clicking on a coreference the annotator can see the chain of elements belonging to the same cluster. For each pronominal anaphor, the five most probable antecedents are linked, and the most probable is highlighted. The annotator needs only to choose the correct one. 4 The Coreference Resolution Process The input of the coreference resolution module consists of a part of the EPEC corpus where each word of the corpus contains its form, lemma, category, POS and morphosyntactic features such as case and number. In addition, NPs are also labeled in the corpus. We only take into account NPs as potential mentions to be included in a coreference chain. The boundaries of these NPs are defined using three labels (NP, NPB, and NPE): if the NP contains more than a word, one label indicates the beginning [NPB] and the other one indicates the end [NPE]. Otherwise, if the NP contains only one word, the word is tagged with a unique label [NP] at its end. 117
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A Treebank-based Investigation of I
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participle and a (te-)infinitival c
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Some verbs occur twice, since they
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Profiling Feature Selection for Nam
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prepositional objects (FOPP, OPP).
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the limited size of annotated data
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with high precision typically have
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‘This was “not significantly”
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The preposition durch typically has
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Non-Projective Structures in Indian
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the sequential order of nodes in a
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extra-posed relative clause that ge
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Experiments on Dependency Parsing o
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for mitigating the effects of spars
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obtained with MALTParser is 76.61%
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as a standardised serialisation for
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constituency and dependency, possib
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SynAF and/or in . However, they sha
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shows how some elements that are no
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Example
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Chinese) as the direct object NP.
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Example 15: Tokens and Word Forms
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In a second experiment, a dataset w
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[3] Leech G. N., Barnett, R. & Kahr
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Effectively long-distance dependenc
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In French, another case of eLDD is
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Page 149 and 150: 6 Conclusions In this paper we have
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of the 5th International Conference
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Translational divergences and their
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posed so far, Cyrus’ work did not
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allowed an appropriate coverage of
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damentales [...] 10 (the universal
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for all lex_pair(s,t) do if head an
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the treatment of typical translatio
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Building a treebank of noisy user-g
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Phenomenon Attested example Std. co
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Figure 1: French Social Media Bank
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Impact of treebank characteristics
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Det Adj N (a) Danish Det Adj N (b)
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mance is not inconsiderable, as was
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Genitives in Hindi Treebank: An Att
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A genitive can occur with complex p
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quite high. This motivates us towar
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A Treebank-based Investigation of IPP-triggering Verbs in Dutch

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