A PDTB-Styled End-to-End Discourse Parser

We have developed a full discourse parser in the Penn Discourse Treebank (PDTB) style. Our trained parser first identifies all discourse and non-discourse relations, locates and labels their arguments, and then classifies their relation types. When appropriate, the attribution spans to these relations are also determined. We present a comprehensive evaluation from both component-wise and error-cascading perspectives.Comment: 15 pages, 5 figures, 7 table

Discourse Structures and Language Technologies

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 12-16. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16955

The biomedical discourse relation bank

<p>Abstract</p> <p>Background</p> <p>Identification of discourse relations, such as causal and contrastive relations, between situations mentioned in text is an important task for biomedical text-mining. A biomedical text corpus annotated with discourse relations would be very useful for developing and evaluating methods for biomedical discourse processing. However, little effort has been made to develop such an annotated resource.</p> <p>Results</p> <p>We have developed the Biomedical Discourse Relation Bank (BioDRB), in which we have annotated explicit and implicit discourse relations in 24 open-access full-text biomedical articles from the GENIA corpus. Guidelines for the annotation were adapted from the Penn Discourse TreeBank (PDTB), which has discourse relations annotated over open-domain news articles. We introduced new conventions and modifications to the sense classification. We report reliable inter-annotator agreement of over 80% for all sub-tasks. Experiments for identifying the sense of explicit discourse connectives show the connective itself as a highly reliable indicator for coarse sense classification (accuracy 90.9% and F1 score 0.89). These results are comparable to results obtained with the same classifier on the PDTB data. With more refined sense classification, there is degradation in performance (accuracy 69.2% and F1 score 0.28), mainly due to sparsity in the data. The size of the corpus was found to be sufficient for identifying the sense of explicit connectives, with classifier performance stabilizing at about 1900 training instances. Finally, the classifier performs poorly when trained on PDTB and tested on BioDRB (accuracy 54.5% and F1 score 0.57).</p> <p>Conclusion</p> <p>Our work shows that discourse relations can be reliably annotated in biomedical text. Coarse sense disambiguation of explicit connectives can be done with high reliability by using just the connective as a feature, but more refined sense classification requires either richer features or more annotated data. The poor performance of a classifier trained in the open domain and tested in the biomedical domain suggests significant differences in the semantic usage of connectives across these domains, and provides robust evidence for a biomedical sublanguage for discourse and the need to develop a specialized biomedical discourse annotated corpus. The results of our cross-domain experiments are consistent with related work on identifying connectives in BioDRB.</p

Primary and secondary discourse connectives: definitions and lexicons

Starting from the perspective that discourse structure arises from the presence of coherence relations, we provide a map of linguistic discourse structuring devices (DRDs), and focus on those for written text. We propose to structure these items by differentiating between primary and secondary connectives on the one hand, and free connecting phrases on the other. For the former, we propose that their behavior can be described by lexicons, and we show one concrete proposal that by now has been applied to three languages, with others being added in ongoing work. The lexical representations can be useful both for humans (theoretical investigations, transfer to other languages) and for machines (automatic discourse parsing and generation)

Argument Labeling of Discourse Relations using LSTM Neural Networks

A discourse relation can be described as a linguistic unit that is composed of sub-units that, when combined, present more information than the sum of its parts. A discourse relation is usually comprised of two arguments that relate to each other in a given form. A discourse relation may have another optional sub-unit called the discourse connective that connects the two arguments and describes the relationship between the two more explicitly. This is called Explicit Discourse relation. Extracting or labeling arguments present in an explicit discourse relations is a challenging task. In recent years, due to the CoNLL competitions, feature engineering has been applied to allow various machine learning models to achieve an F-measure value of about 55%. However, feature engineering is brittle and hand-crafted, requiring advanced knowledge of linguistics as well as the dataset in question. In this thesis, we propose an approach for segmenting (or identifying the boundaries of) Arg1 and Arg2 without feature engineering. We introduce a Bidirectional Long Short-Term Memory (LSTM) based model for argument labeling. We experimented with multiple configurations of our model. Using the Penn Discourse Treebank (PDTB) dataset, our best model achieved an F1 measure of 23.05% without any feature engineering. This is significantly higher than the 20.52% achieved by the state of the art Recurrent Neural Network (RNN) approach, but significantly lower than the feature based state of the art systems. On the other hand, because our approach learns only from the raw dataset, it is more widely applicable to multiple textual genres and languages