Coreferential information of a candidate , such as the properties of its antecedents , is important for pronoun resolution because it reflects the salience of the candidate in the local discourse . Such information , however , is usually ignored in previous learning-based systems . In this paper we present a trainable model which incorporates coreferential information of candidates into pronoun resolution . Preliminary experiments show that our model will boost the resolution performance given the right antecedents of the candidates . We further discuss how to apply our model in real resolution where the antecedents of the candidate are found by a separate noun phrase resolution module . The experimental results show that our model still achieves better performance than the baseline . In recent years , supervised machine learning approaches have been widely explored in reference resolution and achieved considerable success ( Ge et al . , 1998 ; Soon et al . , 2001 ; Ng and Cardie , 2002 ; Strube and Muller , 2003 ; Yang et al . , 2003 ) . Most learning-based pronoun resolution systems determine the reference relationship between an anaphor and its antecedent candidate only from the properties of the pair . The knowledge about the context of anaphor and antecedent is nevertheless ignored . However , research in centering theory ( Sidner , 1981 ; Grosz et al . , 1983 ; Grosz et al . , 1995 ; Tetreault , 2001 ) has revealed that the local focusing ( or centering ) also has a great effect on the processing of pronominal expressions . The choices of the antecedents of pronouns usually depend on the center of attention throughout the local discourse segment ( Mitkov , 1999 ) . To determine the salience of a candidate in the local context , we may need to check the coreferential information of the candidate , such as the existence and properties of its antecedents . In fact , such information has been used for pronoun resolution in many heuristicbased systems . The S-List model ( Strube , 1998 ) , for example , assumes that a co-referring candidate is a hearer-old discourse entity and is preferred to other hearer-new candidates . In the algorithms based on the centering theory ( Brennan et al . , 1987 ; Grosz et al . , 1995 ) , if a candidate and its antecedent are the backwardlooking centers of two subsequent utterances respectively , the candidate would be the most preferred since the CONTINUE transition is always ranked higher than SHIFT or RETAIN . In this paper , we present a supervised learning-based pronoun resolution system which incorporates coreferential information of candidates in a trainable model . For each candidate , we take into consideration the properties of its antecedents in terms of features ( henceforth backward features ) , and use the supervised learning method to explore their influences on pronoun resolution . In the study , we start our exploration on the capability of the model by applying it in an ideal environment where the antecedents of the candidates are correctly identified and the backward features are optimally set . The experiments on MUC-6 ( 1995 ) and MUC-7 ( 1998 ) corpora show that incorporating coreferential information of candidates boosts the system performance significantly . Further , we apply our model in the real resolution where the antecedents of the candidates are provided by separate noun phrase resolution modules . The experimental results show that our model still outperforms the baseline , even with the low recall of the non-pronoun resolution module . The remaining of this paper is organized as follows . Section 2 discusses the importance of the coreferential information for candidate evaluation . Section 3 introduces the baseline learning framework . Section 4 presents and evaluates the learning model which uses backward fea-tures to capture coreferential information , while Section 5 proposes how to apply the model in real resolution . Section 6 describes related research work . Finally , conclusion is given in Section 7 . In this paper we have proposed a model which incorporates coreferential information of candi-dates to improve pronoun resolution . When evaluating a candidate , the model considers its adjacent antecedent by describing its properties in terms of backward features . We first examined the effectiveness of the model by applying it in an optimal environment where the closest antecedent of a candidate is obtained correctly . The experiments show that it boosts the success rate of the baseline system for both MUC-6 ( 4.7 % ) and MUC-7 ( 3.5 % ) . Then we proposed how to apply our model in the real resolution where the antecedent of a non-pronoun is found by an additional non-pronoun resolution module . Our model can still produce Success improvement ( 4.7 % for MUC-6 and 1.8 % for MUC-7 ) against the baseline system , despite the low recall of the non-pronoun resolution module . In the current work we restrict our study only to pronoun resolution . In fact , the coreferential information of candidates is expected to be also helpful for non-pronoun resolution . We would like to investigate the influence of the coreferential factors on general NP reference resolution in our future work .

Challenge: Existing models only use pair-based features but not context for pronoun resolution regardless of its utility.
Approach: They propose a supervised approach that uses coreferential information of candidates from the local context.
Outcome: The proposed method improves over the baselines both in ideal and practical setups.