Corpus-Based Learning for Noun Phrase Coreference Resolution

In this paper, we present a learning approach for coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not just pronouns but rather general noun phrases. In contrast to previous work, we attempt to evaluate our approach on a common data set, the MUC-6 coreference corpus. We obtained encouraging results, indicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to non-learning approaches. 1 I n t r o d u c t i o n Coreference resolution refers to the process of determining if two expressions in natural language refer to the same entity in the world. It is an important subtask in natural language processing systems. In particular, information extraction (IE) systems like those built in the DAI:tPA Message Understanding Conferences (Chinchor, 1998; Sundheim, 1995) have revealed that coreference resolution is such a critical component of IE systems that a separate coreference subtask has been defined and evaluated since MUC-6 (Committee, 1995). In this paper, we focus on the task of determining coreference relations as defined in MUC6 (Committee, 1995). Specifically, a coreference relation denotes an identity of reference and holds between two textual elements known as markables, which are nouns, noun phrases, or pronouns. Thus, our coreference task resolves general noun phrases and not just pronouns, unlike in some previous work on anaphora resolution. The ability to link co-referring noun phrases both within and across sentences is critical to discourse analysis and language understanding in general. 2 A L e a r n i n g A p p r o a c h for C o r e f e r e n c e R e s o l u t i o n We adopt a corpus-based, learning approach for noun phrase coreference resolution. In this approach, we need a relatively small corpus of training documents that have been annotated with coreference chains of noun phrases. All possible markables in a training document are determined by a pipeline of language processing modules, and training examples in the form of feature vectors are generated for appropriate pairs of markables. These training examples are then given to a learning algorithm to build a classifier. To determine the coreference chains in a new document, all markables are determined and potential pairs of co-referring markables are presented to the classifier which will decide whether the two markables actually co-refer. We give the details of these steps in the following subsections. 2.1 D e t e r m i n a t i o n o f Markab le s A pre-requisite for coreference resolution is to obtain most, if not all, of the possible markables in a raw input text. To determine the markables, a pipeline of natural language processing (NLP) modules is used. They consist of sentence segmentation, tokenization, morphological analysis, part-of-speech tagging, noun phrase identification, named entity recognition, and semantic class determination. As far as coreference resolution is concerned, the goal of these NLP modules is to determine the boundary of the markables, and to provide the necessary in.formation about each markable for subsequent generation of features in the training examples. Our part-of-speech tagger is a standard sta-