We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals

We compare two ways of obtaining lexical knowledge for antecedent selection in other-anaphora and definite noun phrase coreference. Specifically, we compare an algorithm that relies on links encoded in the manually created lexical hierarchy WordNet and an algorithm that mines corpora by means of shallow lexico-semantic patterns. As corpora we use the British National Corpus (BNC), as well as the Web, which has not been previously used for this task. Our results show that (a) the knowledge encoded in WordNet is often insufficient, especially for anaphor– antecedent relations that exploit subjective or context-dependent knowledge; (b) for otheranaphora, the Web-based method outperforms the WordNet-based method; (c) for definite NP coreference, the Web-based method yields results comparable to those obtained using WordNet over the whole data set and outperforms the WordNet-based method on subsets of the data set; (d) in both case studies, the BNC-based method is worse than the other methods because of data sparseness. Thus, in our studies, the Web-based method alleviated the lexical knowledge gap often encountered in anaphora resolution and handled examples with context-dependent relations between anaphor and antecedent. Because it is inexpensive and needs no hand-modeling of lexical knowledge, it is a promising knowledge source to integrate into anaphora resolution systems. 1.

Abstract. When implementing a tutoring system that attempts a deep understanding of students ’ natural language explanations, there are three basic approaches to choose between; symbolic, in which sentence strings are parsed using a lexicon and grammar; statistical, in which a corpus is used to train a text classifier; and hybrid, in which rich, symbolically produced features supplement statistical training. Because each type of approach requires different amounts of domain knowledge preparation and provides different quality output for the same input, we describe a method for heuristically combining multiple natural language understanding approaches in an attempt to use each to its best advantage. We explore two basic models for combining approaches in the context of a tutoring system; one where heuristics select the first satisficing representation and another in which heuristics select the highest ranked representation. 1

In this paper, we view coreference resolution as a problem of ranking candidate partitions generated by different coreference systems. We propose a set of partition-based features to learn a ranking model for distinguishing good and bad partitions. Our approach compares favorably to two state-of-the-art coreference systems when evaluated on three standard coreference data sets. 1

Traditionally, coreference resolution is done by mining the reference relationships between NP pairs. However, an individual NP usually lacks adequate description information of its referred entity. In this paper, we propose a supervised learning-based approach which does coreference resolution by exploring the relationships between NPs and coreferential clusters. Compared with individual NPs, coreferential clusters could provide richer information of the entities for better rules learning and reference determination. The evaluation done on MEDLINE data set shows that our approach outperforms the baseline NP-NP based approach in both recall and precision. 1

This paper presents a probabilistic model for resolution of non-pronominal anaphora in biomedical texts. The model seeks to find the antecedents of anaphoric expressions, both coreferent and associative ones, and also to identify discourse-new expressions. We consider only the noun phrases referring to biomedical entities. The model reaches state-of-the art performance: 56-69 % precision and 54-67 % recall on coreferent cases, and reasonable performance on different classes of associative cases. 1

In this paper we present a noun phrase coreference resolution system which aims to enhance the identification of the coreference realized by string matching. For this purpose, we make two extensions to the standard learning-based resolution framework. First, to improve the recall rate, we introduce an additional set of features to capture the different matching patterns between noun phrases. Second, to improve the precision, we modify the instance selection strategy to allow nonanaphors to be included during training instance generation. The evaluation done on MEDLINE data set shows that the combination of the two extensions provides significant gains in the F-measure. 1

Reference resolution is a major component of any natural language system. In the past 30 years significant progress has been made in coreference resolution. However, there is more anaphora in texts than coreference. I present a computational treatment of other-anaphora, i.e., referential noun phrases (NPs) with non-pronominal heads modified by "other" or "another": [. . . ] the move is designed to more accurately reflect the value of products and to put steel on more equal footing with other commodities.

This paper presents a machine learning approach to the resolution of coreferential relations between nominal constituents in Dutch. It is the first significant automatic approach to the resolution of coreferential relations between nominal constituents for this language. The corpusbased strategy was enabled by the annotation of a substantial corpus (ca. 12,500 noun phrases) of Dutch news magazine text with coreferential links for pronominal, proper noun and common noun coreferences. Based on the hypothesis that different types of information sources contribute to a correct resolution of different types of coreferential links, we propose a modular approach in which a separate module is trained per NP type. 1 The task of coreference resolution Although largely unexplored for Dutch, automatic coreference 1 resolution is a research area which is becoming increasingly popular in natural language processing (NLP) research. It is a weakness and therefore a key task in applications such as machine translation, automatic summarization and information extraction for which text understanding is of crucial importance.

resolution