Semantic Role Labeling using Maximum Entropy Model

Semantic Role Labeling using Maximum Entropy Model Joon-Ho Lim, Young-Sook Hwang, So-Young Park, Hae-Chang Rim Department of Computer Science & Engineering Korea University 5-ka, Anam-dong, SEOUL, 136-701, KOREA {jhlim, yshwang, ssoya, rim}@nlp.korea.ac.kr Abstract In this paper, we propose a semantic role label- ing method using a maximum entropy model, which enables not only to exploit rich features but also to alleviate the data sparseness prob- lem in a well-founded model. For applying the maximum entropy model to semantic role la- beling, we take a incremental approach as fol- lows: firstly, the semantic roles are assigned to the arguments in the immediate clause includ- ing a predicate, and then, the semantic roles are assigned to the arguments in the upper clauses by using previously assigned labels. The exper- imental result shows that the proposed method has about 64.76% (F1-measure) on the test set. 1 Introduction The semantic role represents the relationship between a predicate and an argument. It provides a general semantic interpretation of the sentence, and it can play a key role in NLP. The shared task of CoNLL-2004 concerns the automatic semantic role labeling (Carreras, 2004). The challenge for this task is to come forward with machine learning approaches which based on only partial syntactic information such as words, POS tags, chunks, clauses, and named entities. Some machine learning approaches for semantic role labeling have been previously developed (Gildea, 2002; Pradhan, 2003; Thompson, 2003). Gildea (2002) pro- posed a probabilistic discriminative model to assign a semantic roles to the constituent. However, it needs a complex interpolation for smoothing because of the data sparseness problem. Pradhan (2003) applied a support vector machine to semantic role labeling, but if it use a polynomial kernel function for the dependencies be- tween features, it requires high computational complex- ity. Futhermore, becuase the SVM is a binary classifier, one-vs-rest or pairwise method is required for multi-class classification. Thompson (2003) proposed a probabilis- tic generative model which the constituents is generated by the semantic roles. In this model, because a con- stituent depends only on the role that generated it, and constituents are independent of each other, so this model can not utilize contextual information or a relational in- formation between the constituent and the predicate. In this paper, we propose a semantic role labeling method using a maximum entropy model. It is moti- vated by the thought of that for building a successful model, some knowledge of the task are reflected into the model based on the machine learning technique. In this method, we try to combine the structural linguistic knowledge linking syntax to semantics into the machine learning technique. It is realized in terms of two aspects: one is the model framework, the other is the design of feature sets. First of all, for the model framework, we uti- lize the syntactic knowledge of representing the semantic roles in a clause: the arguments of a predicate are located in the immediate clause or the upper clauses. Secondly, for the feature sets, we consider …

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