  Anaphora resolution is a challenging task which has been the interest of NLP
researchers for a long time. Traditional resolution techniques like eliminative
constraints and weighted preferences were successful in many languages.
However, they are ineffective in free word order languages like most SouthAsian
languages.Heuristic and rule-based techniques were typical in these languages,
which are constrained to context and domain.In this paper, we venture a new
strategy us-ing neural networks for resolving anaphora in human-human
dialogues. The architecture chiefly consists of three components, a shallow
parser for extracting features, a feature vector generator which produces the
word embed-dings, and a neural network model which will predict the antecedent
mention of an anaphora.The system has been trained and tested on Telugu
conversation corpus we generated. Given the advantage of the semantic
information in word embeddings and appending actor, gender, number, person and
part of plural features the model has reached an F1-score of 86.
