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Developing a Large Scale Framenet for Italian: the Iframenet Experience

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Developing a Large Scale Framenet for Italian: the Iframenet Experience Developing a large scale FrameNet for Italian: the IFrameNet experience Roberto Basili° Silvia Brambilla§ Danilo Croce° Fabio Tamburini§ ° § Dept. of Enterprise Engineering Dept. of Classic Philology and Italian Studies University of Rome Tor Vergata University of Bologna {basili,croce}@info.uniroma2.it [email protected], [email protected] ian Portuguese, German, Spanish, Japanese, Swe- Abstract dish and Korean. All these projects are based on the idea that English. This paper presents work in pro- most of the Frames are the same among languages gress for the development of IFrameNet, a and that, thanks to this, it is possible to adopt large-scale, computationally oriented, lexi- Berkeley’s Frames and FEs and their relations, cal resource based on Fillmore’s frame se- with few changes, once all the language-specific mantics for Italian. For the development of information has been cut away (Tonelli et al. 2009, IFrameNet linguistic analysis, corpus- Tonelli 2010). processing and machine learning techniques With regard to Italian, over the past ten years are combined in order to support the semi- several research projects have been carried out at automatic development and annotation of different universities and Research Centres. In par- the resource. ticular, the ILC-CNR in Pisa (e.g. Lenci et al. 2008; Johnson and Lenci 2011), FBK in Trento (e.g. Italiano. Questo articolo presenta un work Tonelli et al. 2009, Tonelli 2010) and the Universi- in progress per lo sviluppo di IFrameNet, ty of Rome, Tor Vergata (e.g. Pennacchiotti et al. una risorsa lessicale ad ampia copertura, 2008, Basili et al. 2009) proposed automatic or computazionalmente orientata, basata sulle semiautomatic methods to develop an Italian teorie di Semantica dei Frame proposte da FrameNet. However, as of today, a resource even Fillmore. Per lo sviluppo di IFrameNet so- remotely equivalent to Berkeley’s FrameNet (BFN) no combinate analisi linguistica, corpus- is still missing. processing e tecniche di machine learning al As a lexical resource of this kind is useful in fine di semi-automatizzare lo sviluppo della many computational applications (such as Human- risorsa e il processo di annotazione. Robot interaction), a new effort is currently being jointly made at the universities of Bologna and 1 Introduction Roma, Tor Vergata. The IFrameNet project aims to develop a large-coverage FrameNet-like resource Firstly developed at the University of Berkeley for Italian, relying on robust and scalable methods, (California) in 1997, FrameNet adopts theories in which the automatic corpus processing is con- from Frame Semantics (Fillmore 1976, 1982, sistently integrated with manual lexical analysis. It 1985) to NLP and explains words’ meanings ac- builds upon the achievements of previous projects cording to the semantic frames they evoke. It illus- that automatically harvested FrameNet LUs ex- trates semantic frames (i.e. schematizations of pro- ploiting both distributional and WordNet based totypical events, relations or entities in the reality), models (Pennacchiotti et al. 2008). Since the LUs through the involved participants (called frame el- induction is a noisy process, the data thus obtained ements, FEs) and the evoking words (or, better, the need to be manually refined and validated. lexical units, LUs). Moreover, FrameNet aims to The aim is also to provide Sample Sentences for give a valence representation of the lexical units LUs with the highest corpus frequency. On the one and underline the relations between frames and side, they will be derived from already existing between frame elements (Baker et al. 1998). resources such as the HuRIC corpus (Bastianelli The initial American project has since been ex- 2014) or the EvalIta2011 FLaIT task data: FBK set tended to other languages: French, Chinese, Brazil- (Tonelli, Pianta 2008) and ILC set (Lenci et al. 2012). On the other side, candidate sentences will 59 59 also be extracted through semi-automatic distribu- tional and paradigmatic lexical information (i.e. tional analysis of a large corpus - i.e. CORIS (Ros- derived from WordNet) to assign unknown LUs to sini Favretti et al. 2002) - and refined through lin- frames. In particular, distributional models are used guistic analysis and manual validation of data thus to select a list of frames suggested by the corpus’ obtained. evidence and then the plausible lexical senses of the unknown LU are used to re-rank proposed frames. 2 The development of the large scale In order to rely on comparable representations IFrameNet resource for LUs and sentences for transferring semantic information from the former to the latter, we ex- The need for a large-scale resource cannot be satis- ploit Distributional Models (DM) of Lexical Se- fied without resorting to a semi-automatic process mantics, in line with (Pennacchiotti et al. 2008) and for the gathering of linguistic evidence, selection of (De Cao et al. 2008). DMs are intended to acquire lexical examples as well as the annotation of the semantic relationships between words, mainly by targeted texts. This work is thus at the cross roads looking at the word usage. The foundation for these of linguistic theoretical investigation, corpus analy- models is the Distributional Hypothesis (Harris sis and natural language processing. 1954), i.e. words that are used and occur in the On the one hand, the matching between LUs same “contexts” tend to be semantically similar. A and frames is always granted through manual lin- context is a set of words appearing in the neighbor- guistic validation applied to the data in the devel- hood of a target predicate word (e.g. a LU). In this opment stage. For every Frame the correctness of sense, if two predicates share many contexts then the inducted LUs is analysed and the ‘missing’ they can be considered similar in some way. Alt- LUs, that is the BFN LUs’ translations, which are hough different ways for modeling word semantics absent in the inducted LU’s list, are detected. exist (Sahlgren 2006; Pado and Lapata 2007; On the other hand, most choices rely on large Mikolov et al. 2013; Pennington et al. 2014), they sets of corpus examples, as made available by CO- all derive vector representations for words from RIS. Finally, the scaling to large sets of textual ex- more or less complex processing stages of large- amples is supported by automatically searching scale text collections. This kind of approach is ad- candidate items through semantic pre-filtering over vantageous in that it enables the estimation of se- the corpus: frame phenomena are here used as que- mantic relationships in terms of vector similarity. ries while intelligent retrieval and ranking methods From a linguistic perspective, such vectors allow are applied to the corpus material to minimize the for some aspects of lexical semantics to be geo- manual effort involved. metrically modelled, and to provide a useful way In the following section, we will sketch the to represent this information in a machine-readable main stages of the process that integrate the above format. Distributional methods can model different paradigms. semantic relationships, e.g. topical similarities (if vectors are built considering the occurrence of a 2.1 Integrating corpus processing and lexical word in documents) or paradigmatic similarities (if analysis for populating IFrameNet vectors are built considering the occurrence of a word in the (short) contexts of another word The beneficial contribution of the interaction be- (Sahlgren 2006)). In such models, words like run tween corpus processing techniques and lexical and walk are close in the space, while run and read analysis for the semi-automatic expansion of the are likely to be projected in different subspaces. FrameNet resource has been discussed since (Pen- Here, we concentrate on DMs mainly devoted to nacchiotti et al. 2008), where LU induction is pre- modelling paradigmatic relationships, as we are sented as the task of assigning a generic lexical unit more interested in capturing phenomena of quasi not yet present in the FrameNet database (the so- synonymy, i.e. semantic similarity that tends to called unknown LU) to the correct frame(s). The preserve meaning. number of possible classes (i.e. frames) and the problem of multiple assignment make it a challeng- 2.2 The development cycle ing task. This task is discussed in (Pennacchiotti et In the following paragraphs, we outline the different al. 2008, De Cao et al. 2008, Croce and Previtali stages in the development process. Each stage cor- 2010), where different models combine distribu- responds to specific computational processes. 60 60 Figure 1: Three lexical clusters for the frames triggered by the verb abandon.v: pairs closed in the map correspond to (paradigmatic) semantic similar words and frames Validation of existing resources. At this stage, Lexical clustering is important here as specific the existing resources, dating back to previous space regions enclosing the instance vectors of work, are analysed and manually pruned of errors some considered LUs correspond to semantically such as lexical units wrongly assigned to frames coherent lexical subsets. This is a priming function (e.g. ‘asta’ or ‘colmo’ to the Frame for mapping unseen word vectors to frames, as ap- ‘BODY_PARTS’), or words never assigned to their plied in (De Cao et al. 2008): the centroids of the correct frame, for instances the LU ‘piede’ or possibly multiple clusters generated by the known ‘mano’ for the Frame ‘BODY_PARTS’. LUs of a given frame f are used to detect all regions expressing f and thus predict the predicate f over All the acquired Italian LUs have been com- previously unseen words and sentences. Examples pared, frame by frame, to BFN’s ones, using bilin- of semantically coherent regions evoked by the gual dictionaries (e.g. Oxford bilingual dictionary) verb abandon for the English Framenet are report- and WordNet in order to verify the correctness of ed in Fig.
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