Anaphora resolution is complex problem in linguistics and has attracted the attention of many researchers. It is the problem of identifying referents in the discourse. Anaphora Resolution plays an important role in Natural language processing task. This paper completely emphasis on pronominal anaphora resolution for English Language in which pronouns refers to the intended noun in discourse. In this paper two computational models are proposed for anaphora resolution. Resolution of anaphora is based on various factors among which these models use Recency factor and Animistic Knowledge. Recency factor is implemented by using Lappin Leass approach in first model and using Centering approach in second model. Information about animacy is obtained by Gazetteer method. The identification of animistic elements is employed to improve the accuracy of the system. This paper demonstrates experiment conducted by both the models on different data sets from different domains. A comparative result of both the model is summarized and conclusion is drawn for the best suitable model.

Anaphora Resolution is a process of finding referents in discourse. In computational linguistic, Anaphora resolution is complex and challenging task. This paper focuses on pronominal anaphora resolution. It is a subpart of anaphora resolution where pronouns are referred to noun referents. Including anaphora resolution into many applications like automatic summarization, opinion mining, machine translation, question answering systems etc. increase their accuracy by 10%. Related work in this field has been done in many languages. This paper focuses on resolving anaphora for Punjabi language. A model is proposed for resolving anaphora and an experiment is conducted to measure the accuracy of the system. The model uses two factors: Recency and Animistic knowledge. Recency factor works on the concept of Lappin Leass approach and for introducing animistic knowledge gazetteer method is used. The experiment is conducted on a Punjabi story containing more than 1000 words and result is drawn with the future directions.

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