Semantic Role Labeling Using Dependency Trees

Semantic Role Labeling Using Dependency Trees Kadri Hacioglu Center for Spoken Language Research University of Colorado at Boulder hacioglu@cslr.colorado.edu Abstract In this paper, a novel semantic role labeler based on dependency trees is developed. This is ac- complished by formulating the semantic role la- beling as a classification problem of dependency relations into one of several semantic roles. A dependency tree is created from a constituency parse of an input sentence. The dependency tree is then linearized into a sequence of dependency relations. A number of features are extracted for each dependency relation using a predefined lin- guistic context. Finally, the features are input to a set of one-versus-all support vector machine (SVM) classifiers to determine the corresponding semantic role label. We report results on CoNLL2004 shared task data using the represen- tation and scoring scheme adopted for that task. 1 Introduction In semantic role labeling (SRL) the goal is to group sequences of words together and classify them by using semantic labels. For semantic representation we select the predicate-argument structure that ex- ists in most languages. In this structure a word is specified as a predicate and a number of word groups are considered as arguments accompanying the predicate. Those arguments are assigned differ- ent semantic categories depending on the roles that they play with respect to the predicate. We illustrate the predicate-argument structure in Figure 1 for the sentence “We are prepared to pur- sue aggressively completion of this transaction he says” taken from the PropBank corpus. The chosen predicate is the word pursue, and its arguments with their associated word groups are illustrated. Note that the word prepared is another predicate of the sentence possibly with different argument labels attached to the same or different word groups. For example, the word we is A1 of prepared. This proc- ess of selecting a predicate in a sentence, grouping sequences of words and assigning the semantic roles they play with respect to the chosen predicate is of- ten referred to as semantic role labeling. We believe that a highly accurate extraction of this structure is vital for high performance in many NLP tasks such as information extraction, question answering, summarization and machine translation. Figure 1: Predicate-argument structure of sample sentence. Argument labels are in PropBank-style. Semantic role labeling based on predicate- argument structure was first explored in detail by (Gildea and Jurafsky, 2002). Since then several variants of the basic approach have been introduced using different features and different classifiers based on various machine learning techniques (Gildea and Palmer, 2002; Gildea and Hockenmaier, 2003; Surdeanu et. al., 2003; Chen and Rambow, 2003; Fleischman and Hovy, 2003; Hacioglu and Ward, 2003; Thompson et. al., 2003; Pradhan et. al., 2003b; Hacioglu, 2004). Large semantically anno- tated databases, like FrameNet (Baker et.al, 1998) and PropBank (Kingsbury and Palmer, 2002) have been used to train and test the classifiers. Most of those approaches can be divided into one of the fol- lowing three broad classes with respect to the type of tokens classified; …

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