We present in this paper a study on negation in dialogues. In particular, we analyze the peculiarities of negation in dialogues and propose a new method to detect intra-sentential and inter-sentential negation scope and focus in dialogue context. A key element of the solution is to use dialogue context in the form of previous utterances, which is often needed for proper interpretation of negation in dialogue compared to literary, non-dialogue texts. We have modeled the negation scope and focus detection tasks as a sequence labeling tasks and used Conditional Random Field models to label each token in an utterance as being within the scope/focus of negation or not. The proposed negation scope and focus detection method is evaluated on a newly created corpus (called the DeepTutor Negation corpus; DT-Neg). This dataset was created from actual tutorial dialogue interactions between high school students and a state-of-the-art intelligent tutoring system.
This paper describes a framework for recognizing contradictions between multiple text sources by relying on three forms of linguistic information: (a) negation; (b) antonymy; and (c) semantic and pragmatic information associated with the discourse relations. Two views of contradictions are considered, in which a novel method of recognizing contrast and of finding antonymies are described. Contradictions are used for informing fusion operators in question answering. Our experiments show promising results for the detection of contradictions.
Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processing text documents related to the legal domain. In this study, we developed a mechanism to classify the relationships that can be observed among sentences in transcripts of United States court cases. First, we defined relationship types that can be observed between sentences in court case transcripts. Then we classified pairs of sentences according to the relationship type by combining a machine learning model and a rule-based approach. The results obtained through our system were evaluated using human judges. To the best of our knowledge, this is the first study where discourse relationships between sentences have been used to determine relationships among sentences in legal court case transcripts.
Event extraction systems typically locate the role fillers for an event by analyzing sentences in isolation and identifying each role filler independently of the others. We argue that more accurate event extraction requires a view of the larger context to decide whether an entity is related to a relevant event. We propose a bottom-up approach to event extraction that initially identifies candidate role fillers independently and then uses that information as well as discourse properties to model textual cohesion. The novel component of the architecture is a sequentially structured sentence classifier that identifies event-related story contexts. The sentence classifier uses lexical associations and discourse relations across sentences, as well as domain-specific distributions of candidate role fillers within and across sentences. This approach yields state-of-the-art performance on the MUC-4 data set, achieving substantially higher precision than previous systems.