Pronoun Prediction with Linguistic Features and Example Weighing

We present a system submitted to the WMT16 shared task in cross-lingual pronoun prediction, in particular, to the English-to-German and German-to-English sub-tasks. The system is based on a linear classifier making use of features both from the target language model and from linguistically analyzed source and target texts. Furthermore, we apply example weighing in classifier learning, which proved to be beneficial for recall in less frequent pronoun classes. Compared to other shared task participants, our best English-to-German system is able to rank just below the top performing submissions

Hindi Sentiment Analysis

With the evolution of web technology, a huge amount of data is present on the web. In addition to exploring the resources present on web, the users also provide feedback thus generating additional useful data. Thus mining of data and identifying user sentiments is the need of the hour. Sentiment analysis is the natural language processing task that mines information from various text forms such as blogs, reviews and classify them on the basis of polarity such as positive, negative or neutral. Hindi is the national language of India and is spoken by 366 million people across the world. The percentage of web content in Hindi is growing at lightning speed. A lot of research in opinion mining is carried out in English language but there are not many instances of research done in Hindi language. In this paper we have proposed a strategy for classifying given Hindi texts in to different classes and then extract sentiments in terms of positive, negative and neutral for identified classes. Naive Bayes, Modified Maximum entropy are used for classification and HindiSentiWordNet (HSWN) is used to determine the polarity of individual class

Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction

This paper addresses the task of predicting the correct French translations of third-person subject pronouns in English discourse, a problem that is relevant as a prerequisite for machine translation and that requires anaphora resolution. We present an approach based on neural networks that models anaphoric links as latent variables and show that its performance is competitive with that of a system with separate anaphora resolution while not requiring any coreference-annotated training data. This demonstrates that the information contained in parallel bitexts can successfully be used to acquire knowledge about pronominal anaphora in an unsupervised way

Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction

We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should replace a placeholder value in the target-language text, provided in lemmatised and PoS-tagged form. We provided four subtasks, for the English–French and English–German language pairs, in both directions. Eleven teams participated in the shared task; nine for the English–French subtask, five for French–English, nine for English–German, and six for German–English. Most of the submissions outperformed two strong language-model-based baseline systems, with systems using deep recurrent neural networks outperforming those using other architectures for most language pairs

Pronoun-Focused MT and Cross-Lingual Pronoun Prediction: Findings of the 2015 DiscoMT Shared Task on Pronoun Translation

We describe the design, the evaluation setup, and the results of the DiscoMT 2015 shared task, which included two subtasks, relevant to both the machine translation (MT) and the discourse communities: (i) pronoun-focused translation, a practical MT task, and (ii) cross-lingual pronoun prediction, a classification task that requires no specific MT expertise and is interesting as a machine learning task in its own right. We focused on the English–French language pair, for which MT output is generally of high quality, but has visible issues with pronoun translation due to differences in the pronoun systems of the two languages. Six groups participated in the pronoun-focused translation task and eight groups in the cross-lingual pronoun prediction task