Most traditional approaches to anaphora resolution rely heavily on linguistic and domain knowledge. One of the disadvantages of developing a knowledgebased system, however, is that it is a very labourintensive and time-consuming task. This paper presents a robust, knowledge-poor approach to resolving pronouns in technical manuals, which operates on texts pre-processed by a part-of-speech tagger. Input is checked against agreement and for a number of antecedent indicators. Candidates are assigned scores by each indicator and the candidate with the highest score is returned as the antecedent. Evaluation reports a success rate of 89.7% which is better than the suc- cess rates of the approaches selected for comparison and tested on the same data. In addition, preliminary experiments show that the approach can be successfully adapted for other languages with minimum modifications.

this paper gives a comprehensive picture of a complex, yet not explicitly spelled-out theory of discourse coherence, the centering model (Grosz, Joshi, and Weinstein, 1983, 1995) marked a major step in clarifying the relationship between attentional states and (local) discourse segment structure. More precisely, the centering model accounts for the interactions between local coherence and preferential choices of referring expressions. It relates differences in coherence (in part) to varying demands on inferences as required by different types of referring expressions, given a particular attentional state of the hearer in a discourse setting (Grosz, Joshi, and Weinstein 1995, 204-205). The claim is made then that the lower the inference load put on the hearer, the more coherent the underlying discourse appears. The centering model as formulated by Grosz, Joshi, and Weinstein (1995) refines the structure of "centers" of discourse, which are conceived as the representational device for the attentional state at the local level of discourse. They distinguish two basic types of centers, which can be assigned to each utterance Ui--a single backward- looking center, Cb(Ui), and a partially ordered set of discourse entities, the forward- looking centers, Cf(Ui). The ordering on Cf is relevant for determining the Cb. It can be viewed as a salience ranking that reflects the assumption that the higher the ranking of a discourse entity in Cf, the more likely it will be mentioned again in the immediately following utterance. Thus, given an adequate ordering of the discourse entities in Cf, the costs of computations necessary to establish local coherence are minimized

We present a coreference resolver called BABAR that uses contextual role knowledge to evaluate possible antecedents for an anaphor. BABAR uses information extraction patterns to identify contextual roles and creates four contextual role knowledge sources using unsupervised learning. These knowledge sources determine whether the contexts surrounding an anaphor and antecedent are compatible. BABAR applies a Dempster-Shafer probabilistic model to make resolutions based on evidence from the contextual role knowledge sources as well as general knowledge sources. Experiments in two domains showed that the contextual role knowledge improved coreference performance, especially on pronouns. 1

In this paper we focus on how to improve pronoun resolution using the statisticsbased semantic compatibility information. We investigate two unexplored issues that influence the effectiveness of such information: statistics source and learning framework. Specifically, we for the first time propose to utilize the web and the twin-candidate model, in addition to the previous combination of the corpus and the single-candidate model, to compute and apply the semantic information. Our study shows that the semantic compatibility obtained from the web can be effectively incorporated in the twin-candidate learning model and significantly improve the resolution of neutral pronouns. 1

We present an approach to pronoun resolution based on syntactic paths. Through a simple bootstrapping procedure, we learn the likelihood of coreference between a pronoun and a candidate noun based on the path in the parse tree between the two entities. This path information enables us to handle previously challenging resolution instances, and also robustly addresses traditional syntactic coreference constraints. Highly coreferent paths also allow mining of precise probabilistic gender/number information. We combine statistical knowledge with well known features in a Support Vector Machine pronoun resolution classifier. Significant gains in performance are observed on several datasets. 1

Traditional information retrieval systems based on the "bag-of-words" paradigm cannot completely capture the semantic content of documents. Yet it is impossible with current technology to build a practical information access system that fully analyzes and understands unrestricted natural language. However, if we avoid the most complex and processing-intensive natural language understanding techniques, we can construct a large-scale information access system which is capable of processing unrestricted text, largely understanding it, and answering natural language queries with high precision. We believe that ternary expressions are the most suitable representational structure for such a system; they are expressive enough for information retrieval purposes, yet amenable to rapid large-scale indexing.

We present a discriminative method for learning selectional preferences from unlabeled text. Positive examples are taken from observed predicate-argument pairs, while negatives are constructed from unobserved combinations. We train a Support Vector Machine classifier to distinguish the positive from the negative instances. We show how to partition the examples for efficient training with 57 thousand features and 6.5 million training instances. The model outperforms other recent approaches, achieving excellent correlation with human plausibility judgments. Compared to Mutual Information, it identifies 66% more verb-object pairs in unseen text, and resolves 37 % more pronouns correctly in a pronoun resolution experiment. 1

Introduction Anaphora resolution is a complicated problem in Natural Language Processing and has attracted the attention of many researchers. The approaches developed - traditional (from purely syntactic ones to highly semantic and pragmatic ones), alternative (statistic, uncertainty-reasoning etc.) or knowledge-poor, offer only approximate solutions. The paper is an introduction to anaphora resolution offering a brief survey of the major works in the field. 1.1 Basic notions and terminology The etymology of the term "anaphora" goes back to Ancient Greek with "anaphora" (anajora) being a compound word consisting of the separate words ana - back, upstream, back in an upward direction and jora - the act of carrying and denoted the act of carrying back upstream. For Computational Linguists embarking upon research in the field of anaphor resolution, I strongly recommend as a

In this paper we argue that comparative evaluation in anaphora resolution has to be performed using the same pre-processing tools and on the same set of data. The paper proposes an evaluation environment for comparing anaphora resolution algorithms which is illustrated by presenting the results of the comparative evaluation of three methods on the basis of several evaluation measures.

A variety of approaches to annotating reference in corpora have been adopted. This paper reviews four approaches to the annotation of reference in corpora. Following this we present a variety of results from one annotated corpus, the UCREL anaphoric treebank, relevant to automated reference resolution.