Automatic sense prediction for implicit discourse relations in text

@inproceedings{Pitler2009AutomaticSP,
  title={Automatic sense prediction for implicit discourse relations in text},
  author={Emily Pitler and Annie Louis and Ani Nenkova},
  booktitle={ACL},
  year={2009}
}
We present a series of experiments on automatically identifying the sense of implicit discourse relations, i.e. relations that are not marked with a discourse connective such as "but" or "because". We work with a corpus of implicit relations present in newspaper text and report results on a test set that is representative of the naturally occurring distribution of senses. We use several linguistically informed features, including polarity tags, Levin verb classes, length of verb phrases… Expand
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References

SHOWING 1-10 OF 29 REFERENCES
Easily Identifiable Discourse Relations
We present a corpus study of local discourse relations based on the Penn Discourse Tree Bank, a large manually annotated corpus of explicitly or implicitly realized relations. We show that whileExpand
Representing Discourse Coherence: A Corpus-Based Study
TLDR
A method for annotating discourse coherence structures that was used to manually annotate a database of 135 texts from the Wall Street Journal and the AP Newswire and found many different kinds of crossed dependencies, as well as many nodes with multiple parents. Expand
Using automatically labelled examples to classify rhetorical relations: an assessment
TLDR
Whether automatically labelled, lexically marked examples are really suitable training material for classifiers that are then applied to unmarked examples is tested and some evidence that this behaviour is largely independent of the classifiers used and seems to lie in the data itself is found. Expand
Building and Refining Rhetorical-Semantic Relation Models
TLDR
This work extends the work of Marcu and Echihabi (2002) by refining the training and classification process using parameter optimization, topic segmentation and syntactic parsing, and finds that each of these techniques results in improved relation classification accuracy. Expand
An Unsupervised Approach to Recognizing Discourse Relations
TLDR
It is shown that discourse relation classifiers trained on examples that are automatically extracted from massive amounts of text can be used to distinguish between some of these relations with accuracies as high as 93%, even when the relations are not explicitly marked by cue phrases. Expand
Classification of Discourse Coherence Relations: An Exploratory Study using Multiple Knowledge Sources
TLDR
This approach considers, and determines the contributions of, a variety of syntactic and lexico-semantic features in discourse coherence relations and achieves 81% accuracy on the task of discourse relation type classification and 70% accuracy in relation identification. Expand
English Verb Classes and Alternations: A Preliminary Investigation
TLDR
Beth Levin shows how identifying verbs with similar syntactic behavior provides an effective means of distinguishing semantically coherent verb classes, and isolates these classes by examining verb behavior with respect to a wide range of syntactic alternations that reflect verb meaning. Expand
Coherence and Coreference
TLDR
In this paper, formal definitions are given for several coherence relations, based on the operations of an inference system; that is, the relations between successive portions of a discourse are characterized in terms of the inferences that can be drawn from each. Expand
The classification of coherence relations and their linguistic markers: An exploration of two languages
Abstract It has become popular among discourse linguists to explain a text's coherence by identifying ‘coherence relations’ which apply at various levels between its component spans. However, thereExpand
Automatic Detection of Causal Relations for Question Answering
TLDR
A method for the automatic detection and extraction of causal relations and an inductive learning approach to the automatic discovery of lexical and semantic constraints necessary in the disambiguation of causal Relations that are then used in question answering. Expand
...
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