Introduction to Discourse Relation Parsing and Treebanking (

Zeldes, Amir; Das, Debopam; Maziero, Erick Galani; Antônio, Juliano Desiderato; Iruskieta, Mikel

doi:10.18653/v1/w19-2701

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…The tokenization scheme used in the processing of the corpus was based on orthographic words rather than on syntactic words, as used in some parsers like YAP (More et al, 2019) and UD HTB (Zeldes et al, 2022). While such morpho-syntactic parsers are valuable for analyzing Hebrew texts in general, it is important to note that they were trained on normative native Hebrew and are therefore less suitable for parsing learner language, which may contain complex mixtures of errors on all levels of linguistic analysis.…”

Section: Processingmentioning

confidence: 99%

Analyzing learner language: the case of the Hebrew Learner Essay Corpus

Gafni,

Herzig Sheinfux,

Klunover

et al. 2024

Lang Resources & Evaluation

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We present the Hebrew Learner Essay Corpus (HELEECS): an annotated corpus of Hebrew language argumentative essays authored by prospective higher-education students. The corpus includes essays by two main populations: (1) essays by native speakers of Hebrew, written as part of the psychometric exam that is used to assess their future success in academic studies; (2) essays by non-native speakers of Hebrew, with three different native languages (Arabic, French, and Russian), that were written as part of a language aptitude test. The corpus is uniformly encoded and stored. The non-native essays were annotated with target hypotheses (i.e., hypothesized intended formulations in standard written Hebrew). The corpus is available for research purposes upon request. We describe the corpus and the error correction and annotation schemes used in its analysis. In addition to introducing this new resource, we discuss the challenges of identifying and analyzing non-native language use. Among these challenges are determining whether the language used in a particular utterance is native-like, and determining the target hypothesis when language use is non-native-like. We propose various ways for dealing with these challenges.

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Section: Processingmentioning

confidence: 99%

Analyzing learner language: the case of the Hebrew Learner Essay Corpus

Gafni,

Herzig Sheinfux,

Klunover

et al. 2024

Lang Resources & Evaluation

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RST Discourse Parser for Russian: An Experimental Study of Deep Learning Models

Chistova¹,

Shelmanov

Pisarevskaya³

et al. 2021

Lecture Notes in Computer Science

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This work presents the first fully-fledged discourse parser for Russian based on the Rhetorical Structure Theory of Mann and Thompson (1988). For the segmentation, discourse tree construction, and discourse relation classification we employ deep learning models. With the help of multiple word embedding techniques, the new state of the art for discourse segmentation of Russian texts is achieved. We found that the neural classifiers using contextual word representations outperform previously proposed feature-based models for discourse relation classification. By ensembling both methods, we are able to further improve the performance of the discourse relation classification achieving the new state of the art for Russian.

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A survey on narrative extraction from textual data

et al. 2023

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Narratives are present in many forms of human expression and can be understood as a fundamental way of communication between people. Computational understanding of the underlying story of a narrative, however, may be a rather complex task for both linguists and computational linguistics. Such task can be approached using natural language processing techniques to automatically extract narratives from texts. In this paper, we present an in depth survey of narrative extraction from text, providing a establishing a basis/framework for the study roadmap to the study of this area as a whole as a means to consolidate a view on this line of research. We aim to fulfill the current gap by identifying important research efforts at the crossroad between linguists and computer scientists. In particular, we highlight the importance and complexity of the annotation process, as a crucial step for the training stage. Next, we detail methods and approaches regarding the identification and extraction of narrative components, their linkage and understanding of likely inherent relationships, before detailing formal narrative representation structures as an intermediate step for visualization and data exploration purposes. We then move into the narrative evaluation task aspects, and conclude this survey by highlighting important open issues under the domain of narratives extraction from texts that are yet to be explored.

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Introduction to Discourse Relation Parsing and Treebanking (

Cited by 5 publications

References 38 publications

Analyzing learner language: the case of the Hebrew Learner Essay Corpus

Analyzing learner language: the case of the Hebrew Learner Essay Corpus

RST Discourse Parser for Russian: An Experimental Study of Deep Learning Models

A survey on narrative extraction from textual data

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