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Yu, Yue; Zhu, Yilun; Liu, Yang; Liu, Yan; Peng, Siyao; Gong, Mackenzie; Zeldes, Amir

doi:10.18653/v1/w19-2717

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…In addition, they use a LSTM-based method (based on Keras [50]) with pre-trained word embeddings [51]. GumDrop segmenter [52] is an ensemble of 3 modules: a) The sub-tree module focuses on dependency sub-graphs, looking at a trigram around the potential split point. b) The BoW-Counter module, which predicts the number of segments in each sentence using Ridge regressor with regularization.…”

Section: Related Workmentioning

confidence: 99%

Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection tool

et al. 2019

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Lately, discourse structure has received considerable attention due to the benefits its application offers in several NLP tasks such as opinion mining, summarization, question answering, text simplification, among others. When automatically analyzing texts, discourse parsers typically perform two different tasks: i ) identification of basic discourse units (text segmentation) ii ) linking discourse units by means of discourse relations, building structures such as trees or graphs. The resulting discourse structures are, in general terms, accurate at intra-sentence discourse-level relations, however they fail to capture the correct inter-sentence relations. Detecting the main discourse unit (the Central Unit) is helpful for discourse analyzers (and also for manual annotation) in improving their results in rhetorical labeling. Bearing this in mind, we set out to build the first two steps of a discourse parser following a top-down strategy: i ) to find discourse units, ii ) to detect the Central Unit. The final step, i.e. assigning rhetorical relations, remains to be worked on in the immediate future. In accordance with this strategy, our paper presents a tool consisting of a discourse segmenter and an automatic Central Unit detector.

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Section: Related Workmentioning

confidence: 99%

Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection tool

et al. 2019

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“…With the exception of systems presented at DIS-RPT 2019 (Bourgonje and Schäfer, 2019;Yu et al, 2019;Muller et al, 2019), existing work on segmentation always assumed gold sentences, e.g. (Wang et al, 2018;Lukasik et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Multi-lingual Discourse Segmentation and Connective Identification: MELODI at Disrpt2021

Ezzabady¹,

Muller²,

Braud³

2021

Proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021)

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We present an approach for discourse segmentation and discourse connective identification, both at the sentence and document level, within the DISRPT 2021 shared task, a multi-lingual and multi-formalism evaluation campaign. 1 Building on the most successful architecture from the 2019 similar shared task, we leverage datasets in the same or similar languages to augment training data and improve on the best systems from the previous campaign on 3 out of 4 subtasks, with a mean improvement on all 16 datasets of 0.85%. Within the Disrpt 21 campaign the system ranks 3rd overall, very close to the 2nd system, but with a significant gap with respect to the best system, which uses a rich set of additional features. The system is nonetheless the best on languages that benefited from crosslingual training on sentence internal segmentation (German and Spanish).

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The Thai Discourse Treebank: Annotating and Classifying Thai Discourse Connectives

Prasertsom,

Jaroonpol,

Rutherford

2024

Transactions of the Association for Computational Linguistics

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Discourse analysis is a highly applicable area of natural language processing. In English and other languages, resources for discourse-based tasks are widely available. Thai, however, has hitherto lacked such resources. We present the Thai Discourse Treebank, the first, large Thai corpus annotated in the style of the Penn Discourse Treebank. The resulting corpus has over 10,000 sentences and 18,000 instances of connectives in 33 different relations. We release the corpus alongside our list of 148 potentially polysemous discourse connectives with a total of 340 form-sense pairs and their classification criteria to facilitate future research. We also develop models for connective identification and classification tasks. Our best models achieve an F1 of 0.96 in the identification task and 0.46 on the sense classification task. Our results serve as benchmarks for future models for Thai discourse tasks.

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Cited by 7 publications

References 24 publications

Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection tool

Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection tool

Multi-lingual Discourse Segmentation and Connective Identification: MELODI at Disrpt2021

The Thai Discourse Treebank: Annotating and Classifying Thai Discourse Connectives

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