Alan Lee

Attribution and the (Non-)Alignment of Syntactic and Discourse Arguments of Connectives

The annotations of the Penn Discourse Treebank (PDTB) include (1) discourse connectives and their arguments, and (2) attribution of each argument of each connective and of the relation it denotes. Because the PDTB covers the same text as the Penn TreeBank WSJ corpus, syntactic and discourse annotation can be compared. This has revealed significant differences between syntactic structure and discourse structure, in terms of the arguments of connectives, due in large part to attribution. We describe these differences, an algorithm for detecting them, and finally some experimental results. These results have implications for automating discourse annotation based on syntactic annotation.

Sense annotation in the Penn discourse treebank

An important aspect of discourse understanding and generation involves the recognition and processing of discourse relations. These are conveyed by discourse connectives, i.e., lexical items like because and as a result or implicit connectives expressing an inferred discourse relation. The Penn Discourse TreeBank (PDTB) provides annotations of the argument structure, attribution and semantics of discourse connectives. In this paper, we provide the rationale of the tagset, detailed descriptions of the senses with corpus examples, simple semantic definitions of each type of sense tags as well as informal descriptions of the inferences allowed at each level.

Annotating Attribution in the Penn Discourse TreeBank

An emerging task in text understanding and generation is to categorize information as fact or opinion and to further attribute it to the appropriate source. Corpus annotation schemes aim to encode such distinctions for NLP applications concerned with such tasks, such as information extraction, question answering, summarization, and generation. We describe an annotation scheme for marking the attribution of abstract objects such as propositions, facts and eventualities associated with discourse relations and their arguments annotated in the Penn Discourse TreeBank. The scheme aims to capture the source and degrees of factuality of the abstract objects. Key aspects of the scheme are annotation of the text spans signalling the attribution, and annotation of features recording the source, type, scopal polarity, and determinacy of attribution.

A Discourse-Annotated Corpus of Conjoined VPs

English grammars indicate a variety of relations holding between conjoined VPs. VPs conjoined by and evince such senses as Result, Temporal Sequence and Concession. Although all these senses are ones associated with discourse relations, conjoined VPs have not been fully included in discourse annotation. Because of the value of discourse-annotated corpora for developing approaches to automated sense recognition, we have added their annotation to the Penn Discourse TreeBank. This paper describes how tokens were identified; how the process of span and sense annotation was modified and extended in order to keep the annotation of intra-sentential multi-clausal structures consistent with the rest of the corpus; and what the resulting corpus looks like, in terms of token frequency and common sense patterns.

A Pilot Annotation to Investigate Discourse Connectivity in Biomedical Text

The goal of the Penn Discourse Treebank (PDTB) project is to develop a large-scale corpus, annotated with coherence relations marked by discourse connectives. Currently, the primary application of the PDTB annotation has been to news articles. In this study, we tested whether the PDTB guidelines can be adapted to a different genre. We annotated discourse connectives and their arguments in one 4,937-token full-text biomedical article. Two linguist annotators showed an agreement of 85% after simple conventions were added. For the remaining 15% cases, we found that biomedical domain-specific knowledge is needed to capture the linguistic cues that can be used to resolve inter-annotator disagreement. We found that the two annotators were able to reach an agreement after discussion. Thus our experiments suggest that the PDTB annotation can be adapted to new domains by minimally adjusting the guidelines and by adding some further domain-specific linguistic cues.

Bridging Sentential and Discourse-level Semantics through Clausal Adjuncts

It is in PropBank’s ARGM annotation of clausal adjuncts that sentential semantics meets discourse relation annotation in the Penn Discourse TreeBank. This paper discusses complementarities between the two annotation systems: How PropBank ARGM annotation can be used to seed annotation of additional discourse relations in the PDTB, and how PDTB annotation can be used to refine or enrich PropBank ARGM annotation.