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20172022
most citedHow Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis

26 citations · 40 across the 10 of their papers we have counts for

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cs.CL2021

Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing

Alberto Muñoz-Ortiz, Michalina Strzyz, David Vilares

Different linearizations have been proposed to cast dependency parsing as sequence labeling and solve the task as: (i) a head selection problem, (ii) finding a representation of th…

cs.CL2021

On the logistical difficulties and findings of Jopara Sentiment Analysis

Marvin M. Agüero-Torales, David Vilares, Antonio G. López-Herrera

This paper addresses the problem of sentiment analysis for Jopara, a code-switching language between Guarani and Spanish. We first collect a corpus of Guarani-dominant tweets and d…

cs.CL2021

Bertinho: Galician BERT Representations

David Vilares, Marcos Garcia, Carlos Gómez-Rodríguez

This paper presents a monolingual BERT model for Galician. We follow the recent trend that shows that it is feasible to build robust monolingual BERT models even for relatively low…

cs.CL2020

Bracketing Encodings for 2-Planar Dependency Parsing

Michalina Strzyz, David Vilares, Carlos Gómez-Rodríguez

We present a bracketing-based encoding that can be used to represent any 2-planar dependency tree over a sentence of length n as a sequence of n labels, hence providing almost tota…

cs.CL2020

A Unifying Theory of Transition-based and Sequence Labeling Parsing

Carlos Gómez-Rodríguez, Michalina Strzyz, David Vilares

We define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. This not only establishes a t…

cs.CL2020

Discontinuous Constituent Parsing as Sequence Labeling

David Vilares, Carlos Gómez-Rodríguez

This paper reduces discontinuous parsing to sequence labeling. It first shows that existing reductions for constituent parsing as labeling do not support discontinuities. Second, i…