We present a reformulation of the word pair features typically used for the task of disambiguating implicit relations in the Penn Discourse Treebank. Our word pair features achieve significantly higher performance than the previous formulation when evaluated without additional features. In addition, we present results for a full system using additional features which achieves close to state of the art performance without resorting to gold syntactic parses or to context outside the relation
In order to understand a coherent text, humans infer semantic or logical relations between textual u...
International audienceAutomatically identifying implicit discourse relations requires an in-depth se...
Automatically identifying implicit discourse relations requires an in-depth semantic understanding o...
Abstract This paper presents a detailed comparative framework for assessing the usefulness of unsupe...
The earliest work on automatic detec-tion of implicit discourse relations relied on lexical features...
We present a series of experiments on automatically identifying the sense of implicit discourse rela...
Modern solutions for implicit discourse relation recognition largely build universal models to class...
Abstract(#br)Implicit discourse relation recognition is the performance bottleneck of discourse stru...
We provide a systematic study of previously proposed features for implicit discourse relation identi...
International audienceWe introduce a simple semi-supervised approach to improve implicitdiscourse re...
We present a corpus study of local discourse relations based on the Penn Discourse Tree Bank, a larg...
International audienceDiscourse relation classification has proven to be a hard task, with rather lo...
Implicit discourse relation recognition is a challenging task in the natural language processing fie...
Building discourse parsers is currently a major challenge in Natural Language Processing. The identi...
While explicit discourse connectives can signal coherence relations, a common assumption is that onl...
In order to understand a coherent text, humans infer semantic or logical relations between textual u...
International audienceAutomatically identifying implicit discourse relations requires an in-depth se...
Automatically identifying implicit discourse relations requires an in-depth semantic understanding o...
Abstract This paper presents a detailed comparative framework for assessing the usefulness of unsupe...
The earliest work on automatic detec-tion of implicit discourse relations relied on lexical features...
We present a series of experiments on automatically identifying the sense of implicit discourse rela...
Modern solutions for implicit discourse relation recognition largely build universal models to class...
Abstract(#br)Implicit discourse relation recognition is the performance bottleneck of discourse stru...
We provide a systematic study of previously proposed features for implicit discourse relation identi...
International audienceWe introduce a simple semi-supervised approach to improve implicitdiscourse re...
We present a corpus study of local discourse relations based on the Penn Discourse Tree Bank, a larg...
International audienceDiscourse relation classification has proven to be a hard task, with rather lo...
Implicit discourse relation recognition is a challenging task in the natural language processing fie...
Building discourse parsers is currently a major challenge in Natural Language Processing. The identi...
While explicit discourse connectives can signal coherence relations, a common assumption is that onl...
In order to understand a coherent text, humans infer semantic or logical relations between textual u...
International audienceAutomatically identifying implicit discourse relations requires an in-depth se...
Automatically identifying implicit discourse relations requires an in-depth semantic understanding o...