Semantic Role Labeling Via Integer Linear Programming Inference

Semantic Role Labeling via Integer Linear Programming Inference Vasin Punyakanok Dan Roth Wen-tau Yih Dav Zimak Department of Computer Science University of Illinois at Urbana-Champaign {punyakan,danr,yih,davzimak}@uiuc.edu Abstract We present a system for the semantic role la- beling task. The system combines a machine learning technique with an inference procedure based on integer linear programming that sup- ports the incorporation of linguistic and struc- tural constraints into the decision process. The system is tested on the data provided in CoNLL- 2004 shared task on semantic role labeling and achieves very competitive results. 1 Introduction Semantic parsing of sentences is believed to be an important task toward natural language understand- ing, and has immediate applications in tasks such information extraction and question answering. We study semantic role labeling(SRL). For each verb in a sentence, the goal is to identify all constituents that fill a semantic role, and to determine their roles, such as Agent, Patient or Instrument, and their ad- juncts, such as Locative, Temporal or Manner. The PropBank project (Kingsbury and Palmer, 2002) provides a large human-annotated corpus of semantic verb-argument relations. Specifically, we use the data provided in the CoNLL-2004 shared task of semantic-role labeling (Carreras and M`arquez, 2003) which consists of a portion of the PropBank corpus, allowing us to compare the per- formance of our approach with other systems. Previous approaches to the SRL task have made use of a full syntactic parse of the sentence in or- der to define argument boundaries and to determine the role labels (Gildea and Palmer, 2002; Chen and Rambow, 2003; Gildea and Hockenmaier, 2003; Pradhan et al., 2003; Pradhan et al., 2004; Sur- deanu et al., 2003). In this work, following the CoNLL-2004 shared task definition, we assume that the SRL system takes as input only partial syn- tactic information, and no external lexico-semantic knowledge bases. Specifically, we assume as input resources a part-of-speech tagger, a shallow parser that can process the input to the level of based chunks and clauses (Tjong Kim Sang and Buch- holz, 2000; Tjong Kim Sang and D´ejean, 2001), and a named-entity recognizer (Tjong Kim Sang and De Meulder, 2003). We do not assume a full parse as input. SRL is a difficult task, and one cannot expect high levels of performance from either purely man- ual classifiers or purely learned classifiers. Rather, supplemental linguistic information must be used to support and correct a learning system. So far, machine learning approaches to SRL have incorpo- rated linguistic information only implicitly, via the classifiers’ features. The key innovation in our ap- proach is the development of a principled method to combine machine learning techniques with linguis- tic and structural constraints by explicitly incorpo- rating inference into the decision process. In the machine learning part, the system we present here is composed of two phases. First, a set of argument candidates is produced using two learned classifiers—one to discover beginning po- sitions and one to discover end positions of each argument type. Hopefully, this phase discovers a small …

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