Discourse parsing is the process of discovering the latent relational structure of a long form piece of text and remains a significant open challenge. One of the most difficult tasks in discourse parsing is the classification of implicit discourse relations. Most state-of-the-art systems do not leverage the great volume of unlabeled text available on the web–they rely instead on human annotated...

Recent results by Ng and Cardie (2002a) challenge the idea–advanced, e.g., by Vieira and Poesio (2000)–that including methods for identifying discourse new descriptions in an anaphoric resolver leads to better results. In previous work we analyzed the literature on discourse-new detection that followed Vieira and Poesio’s original work, and identified a set of features that appear to be present...

Implicit discourse relation recognition is difficult due to the absence of explicit discourse connectives between arbitrary spans of text. In this paper, we use language models to predict the discourse connectives between the arguments pair. We present two methods to apply the predicted connectives to implicit discourse relation recognition. One is to use the sense frequency of the specific con...

Discourse Parsing and Sentiment Analysis are two fundamental tasks in Natural Language Processing that have been shown to be mutually beneficial. In this work, we design and compare two Neural models for jointly learning both tasks. In the proposed approach, we first create a vector representation for all the text segments in the input sentence. Next, we apply three different Recursive Neural N...

In the past decade, statistical machine translation (SMT) has been advanced from word-based SMT to phraseand syntax-based SMT. Although this advancement produces significant improvements in BLEU scores, crucial meaning errors and lack of cross-sentence connections at discourse level still hurt the quality of SMT-generated translations. More recently, we have witnessed two active movements in SM...

This introduction tracks the evolution of the definition and role of discourse issues in NLP from the knowledge-intensive “discourse understanding” methods of the 80’s to the recent concern with “accessing contents” in vast document bases via data-intensive methods. As text/discourse linguistics moves toward corpus approaches, also in connection with the development of large text bases and of c...