Joint Conference on EMNLP and CoNLL - Shared Task

Anaphora Resolution is playing more and more important role in Natural Language Processing. There is an increasing need for the development of effective and robust strategies of anaphora resolution to meet the demands of practical applications. However, traditional approaches to anaphora resolution rely heavily on multilevel linguistic knowledge, such as syntactic, semantic, contextual and domain knowledge. It is undoubtedly difficult to acquire such knowledge at present. This paper presents a two-step approach with limited knowledge to resolve pronominal anaphora within Chinese text, which only uses number features, gender features and the features of grammatical roles. In this approach, a filter is firstly used to eliminate those expressions whose features are inconsistent with the pronoun, and thus form a set of potential antecedent candidates; then, a scoring algorithm is employed to calculate score of the candidates, and the candidate with the highest score is selected as the resultant antecedent. The algorithm does not examine each candidate in the set, but automatically determine whether to end the calculation or not by dynamically testing a termination condition, therefore the computational complexity is low. In addition, the approach does not need a deep analysis of the text, and can easily be implemented. Experiment shows the result is satisfactory.

/pdf/robust-pronominal-resolution-within-chinese-text-4vn2k81gqu.pdf

Robust Pronominal Resolution within Chinese Text

This paper presents a mixed deterministic model for coreference resolution in the CoNLL-2012 shared task. We separate the two main stages of our model, mention detection and coreference resolution, into several sub-tasks which are solved by machine learning method and deterministic rules based on multi-filters, such as lexical, syntactic, semantic, gender and number information. We participate in the closed track for English and Chinese, and also submit an open result for Chinese using tools to generate the required features. Finally, we reach the average F1 scores 58.68, 60.69 and 61.02 on the English closed task, Chinese closed and open tasks.

A Mixed Deterministic Model for Coreference Resolution

Coreference resolution is an important research topic in natural language processing, including the coreference resolution of proper nouns, common nouns and pronouns. In this paper, a coreference resolution algorithm of the Chinese noun phrase and the pronoun is proposed that based on maximum entropy model and rules. The use of maximum entropy model can integrate effectively a variety of separate features, on this basis to use rules method to improve the recall rate of digestion, and then use filtering rules to remove "noise" to further improve the accuracy rate of digestion. Experiments show that the F value of the algorithm in a closed test and an open test can reach 85.2% and 76.2% respectively, which improve about 12.9 percentage points and 7.8 percentage points compare with the method of rules respectively.

The Research on Chinese Coreference Resolution Based on Maximum Entropy Model and Rules

Coreference is a common linguistic phenomenon in natural language understanding, it plays an important role in simplifying the expression and linking up the context. In this paper, the algorithm of support vector machines is applied to solve the problem of Chinese coreference, we consider fully the important characteristics which related to coreference and integrate them effectively to build model. In the handling of training data, using data scaling techniques balance the range of characteristic values, and use cross validation to optimize the training parameters of the model. The experimental results show that the F-score of positive instances and negative instances reached 76.80% and 90.91% respectively on the classification model in Lancaster Corpus of Mandarin Chinese.

The Research on Chinese Coreference Resolution Based on Support Vector Machines

Anaphora occurs throughout discourse or dialogue.Their high frequencies make anaphora resolution one key problem in discourse processing which attract attention of increasing researchers.In this article,some issues of anaphora resolution will be discussed,such as basic concepts,special referring phenomena,necessary knowledge on anaphora resolution.Some typical computational models of anaphora resolution and implement technologies will be given as well.

Survey:Computational Models and Technologies in Anaphora Resolution

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Survey:Computational Models and Technologies in Anaphora Resolution