Application of feature propagation to dependency parsing

Bengoetxea, Kepa; Gojenola, Koldo

doi:10.3115/1697236.1697264

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…We describe below the features considered in our study for developing CRAS. The tools used for processing text are Freeling (Padró, Collado, Reese, Lloberes, & Castellón, ) for English, Spanish, French, Catalan, and Italian, as well as Katea (Bengoetxea & Gojenola, ) for Basque.…”

Section: Description Of Featuresmentioning

confidence: 99%

Is cross‐lingual readability assessment possible?

Azpiazu

Pera

2019

Asso for Info Science & Tech

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Most research efforts related to automatic readability assessment focus on the design of strategies that apply to a specific language. These state‐of‐the‐art strategies are highly dependent on linguistic features that best suit the language for which they were intended, constraining their adaptability and making it difficult to determine whether they would remain effective if they were applied to estimate the level of difficulty of texts in other languages. In this article, we present the results of a study designed to determine the feasibility of a cross‐lingual readability assessment strategy. For doing so, we first analyzed the most common features used for readability assessment and determined their influence on the readability prediction process of 6 different languages: English, Spanish, Basque, Italian, French, and Catalan. In addition, we developed a cross‐lingual readability assessment strategy that serves as a means to empirically explore the potential advantages of employing a single strategy (and set of features) for readability assessment in different languages, including interlanguage prediction agreement and prediction accuracy improvement for low‐resource languages.

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Section: Description Of Featuresmentioning

confidence: 99%

Is cross‐lingual readability assessment possible?

Azpiazu

Pera

2019

Asso for Info Science & Tech

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Building Resources for Georgian Treebanking-Based NLP

Kapanadze

Kotzé

Hanneforth

2022

Lecture Notes in Computer Science

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Improving Transition-Based Dependency Parsing of Hindi and Urdu by Modeling Syntactically Relevant Phenomena

Bhat

Sharma

2017

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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In recent years, transition-based parsers have shown promise in terms of efficiency and accuracy. Though these parsers have been extensively explored for multiple Indian languages, there is still considerable scope for improvement by properly incorporating syntactically relevant information. In this article, we enhance transition-based parsing of Hindi and Urdu by redefining the features and feature extraction procedures that have been previously proposed in the parsing literature of Indian languages. We propose and empirically show that properly incorporating syntactically relevant information like case marking, complex predication and grammatical agreement in an arc-eager parsing model can significantly improve parsing accuracy. Our experiments show an absolute improvement of ∼2% LAS for parsing of both Hindi and Urdu over a competitive baseline which uses rich features like part-of-speech (POS) tags, chunk tags, cluster ids and lemmas. We also propose some heuristics to identify ezafe constructions in Urdu texts which show promising results in parsing these constructions.

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Application of feature propagation to dependency parsing

Cited by 3 publications

References 3 publications

Is cross‐lingual readability assessment possible?

Is cross‐lingual readability assessment possible?

Building Resources for Georgian Treebanking-Based NLP

Improving Transition-Based Dependency Parsing of Hindi and Urdu by Modeling Syntactically Relevant Phenomena

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