The Hindi Discourse Relation Bank

Oza, Umangi; Prasad, Rashmi; Kolachina, Sudheer; Sharma, Dipti Misra; Joshi, Aravind K.

doi:10.3115/1698381.1698410

Cited by 23 publications

(13 citation statements)

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“…The PDTB framework has also been adopted for discourse annotation in other languages (e.g., Turkish [85], Hindi [86,87], Chinese [88], Czech [89] and Italian [90]) as well as other domains such as conversational dialogues [90]. …”

Section: Methodsmentioning

confidence: 99%

The biomedical discourse relation bank

et al. 2011

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BackgroundIdentification 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.ResultsWe 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).ConclusionOur 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.

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Section: Methodsmentioning

confidence: 99%

The biomedical discourse relation bank

et al. 2011

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“…Debopam Das, Maite Taboada emergence of interest in DMs has also led to the production of an increasingly large number of text corpora annotated for DMs (most often accompanied by relational annotations) in different languages, based on different discourse frameworks; for instance, PDTB (Penn Discourse Treebank) annotations for: English (Prasad et al, 2008), Arabic (Al-Saif & Markert, 2010), Czech (Rysová et al, 2016), Hindi (Oza et al, 2009), Turkish (Zeyrek et al, 2010) and multilingual (Zeyrek et al, 2018); RST (Rhetorical Structure Theory) for: English (Das et al, 2015), Dutch (Redeker et al, 2012) and Spanish (Maziero et al, 2011); or SDRT (Segmented Discourse Representation Theory) for French (Afantenos et al, 2012).…”

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Multiple Signals of Coherence Relations

Das¹,

Taboada²

2019

discours

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In this paper, we investigate the signalling of coherence relations when they are simultaneously indicated by more than one signal. In particular, we examine the co-occurrence of discourse markers and other relational signals when they are used together to mark a single relation. With the goal to identify the source of the usage of multiple signals, we postulate a twofold hypothesis: the co-occurrence of discourse markers and other textual signals can result from the type of the discourse markers themselves, or it can be triggered by the semantics of the relations in question. We conduct a corpus study, examining instances of multiple signals (co-occurrence of discourse markers and other signals) in the RST Signalling Corpus (Das et al., 2015). We analyze discourse markers that appear as part of multiple signals and also relations that frequently employ multiple signals as their indicators. Our observations suggest that the signalling of relations by multiple signals is a complex phenomenon, since the co-occurrence of discourse markers and other textual signals appears to arise from multiple sources.

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“…A comparison of a small amount of HDRB annotation with PDTB annotation by Oza et al (2009) shows that a significantly larger percentage of discourse relations are conveyed explicitly, but through some means other than a conjunction, discourse adverbial, sentence relativizer, or emphatic particle. Further analysis, along with further analysis of the same phenomenon in the PDTB, should lead to greater understanding of the many ways in which discourse relations are conveyed.…”

Section: Hindimentioning

confidence: 99%

“…The Hindi Discourse Relation Bank (HDRB) (Oza et al 2009) aims to annotate a 200K-word corpus, drawn from a 400K-word corpus of news articles from the Hindi newspaper Amar Ujala whose sentences have been annotated with syntactic dependencies. The style of HDRB discourse annotation is similar to that of the PDTB, with interesting differences.…”

Section: Hindimentioning

confidence: 99%

Discourse structure and language technology

2011

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An increasing number of researchers and practitioners in Natural Language Engineering face the prospect of having to work with entire texts, rather than individual sentences. While it is clear that text must have useful structure, its nature may be less clear, making it more difficult to exploit in applications. This survey of work on discourse structure thus provides a primer on the bases of which discourse is structured along with some of their formal properties. It then lays out the current state-of-the-art with respect to algorithms for recognizing these different structures, and how these algorithms are currently being used in Language Technology applications. After identifying resources that should prove useful in improving algorithm performance across a range of languages, we conclude by speculating on future discourse structure-enabled technology.

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The Hindi Discourse Relation Bank

Cited by 23 publications

References 1 publication

The biomedical discourse relation bank

The biomedical discourse relation bank

Multiple Signals of Coherence Relations

Discourse structure and language technology

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