Semantic Role Labelling With Chunk Sequences

Semantic Role Labelling With Chunk Sequences Ulrike Baldewein, Katrin Erk, Sebastian Padó Saarland University Saarbrücken, Germany {ulrike,erk,pado}@coli.uni-sb.de Detlef Prescher University of Amsterdam Amsterdam, The Netherlands prescher@science.uva.nl Abstract We describe a statistical approach to semantic role labelling that employs only shallow infor- mation. We use a Maximum Entropy learner, augmented by EM-based clustering to model the fit between a verb and its argument can- didate. The instances to be classified are se- quences of chunks that occur frequently as ar- guments in the training corpus. Our best model obtains an F score of 51.70 on the test set. 1 Introduction This paper describes a statistical approach to semantic role labelling addressing the CoNLL shared task 2004, which is based on the the current release of the English PropBank data (Kingsbury et al., 2002). For further de- tails of the task, see (Carreras and Màrquez, 2004). We address the main challenge of the task, the absence of deep syntactic information, with three main ideas: � Proper constituents being unavailable, we use chunk sequences as instances for classification. � The classification is performed by a maximum en- tropy model, which can integrate features from het- erogeneous data sources. � We model the fit between verb and argument can- didate by clusters induced with EM on the training data, which we use as features during classification. Sections 2 through 4 describe the systems’ architec- ture. First, we compute chunk sequences for all sentences (Sec. 2). Then, we classify these sequences with max- imum entropy models (Sec. 3). Finally, we determine the most probable chain of sequences covering the whole sentence (Sec. 4). Section 5 discusses the impact of dif- ferent parameters and gives final results. 2 Chunk Sequences as Instances All studies of semantic role labelling we are aware of have used constituents as instances for classification. However, constituents are not available in the shallow syntactic information provided by this task. Two other levels of granularity are available in the data: words and chunks. In a pretest, we found that words are too fine grained, such that learners find it very difficult to identify argument boundaries on the word level. Chunks, too, are problematic, since one third of the arguments span more than one chunk, and for one tenth of the arguments the boundaries do not coincide with any chunk boundaries. We decided to use chunk sequences as instances for classification. They can describe multi-chunk and part- chunk arguments, and by approximating constituents, they allow the use of linguistically informed features. In the sentence in Figure 1, Britain’s manufacturing indus- try forms a sequence of type NP_NP. To make sequences more distinctive, we conflate whole clauses embedded deeper than the target to S: For the target transform- ing, we characterise the sequence for to boost exports as S rather than VP_NP. An argument boundary inside a chunk is indicated by the part of speech of the last in- cluded word: For boost the sequence is VP(NN). To determine “good” sequences, we collected argu- ment …

Content truncated to 500 words