Search Results for author: David Vilares

Found 38 papers, 20 papers with code

From Partial to Strictly Incremental Constituent Parsing

no code implementations5 Feb 2024 Ana Ezquerro, Carlos Gómez-Rodríguez, David Vilares

We study incremental constituent parsers to assess their capacity to output trees based on prefix representations alone.

4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees

no code implementations22 Oct 2023 Carlos Gómez-Rodríguez, Diego Roca, David Vilares

We introduce an encoding for parsing as sequence labeling that can represent any projective dependency tree as a sequence of 4-bit labels, one per word.

On the Challenges of Fully Incremental Neural Dependency Parsing

1 code implementation28 Sep 2023 Ana Ezquerro, Carlos Gómez-Rodríguez, David Vilares

Since the popularization of BiLSTMs and Transformer-based bidirectional encoders, state-of-the-art syntactic parsers have lacked incrementality, requiring access to the whole sentence and deviating from human language processing.

Dependency Parsing Sentence

Assessment of Pre-Trained Models Across Languages and Grammars

1 code implementation20 Sep 2023 Alberto Muñoz-Ortiz, David Vilares, Carlos Gómez-Rodríguez

We present an approach for assessing how multilingual large language models (LLMs) learn syntax in terms of multi-formalism syntactic structures.

Dependency Parsing

Contrasting Linguistic Patterns in Human and LLM-Generated Text

no code implementations17 Aug 2023 Alberto Muñoz-Ortiz, Carlos Gómez-Rodríguez, David Vilares

We conduct a quantitative analysis contrasting human-written English news text with comparable large language model (LLM) output from 4 LLMs from the LLaMa family.

Language Modelling Large Language Model +1

Another Dead End for Morphological Tags? Perturbed Inputs and Parsing

1 code implementation24 May 2023 Alberto Muñoz-Ortiz, David Vilares

The usefulness of part-of-speech tags for parsing has been heavily questioned due to the success of word-contextualized parsers.

Adversarial Attack

Parsing linearizations appreciate PoS tags - but some are fussy about errors

no code implementations27 Oct 2022 Alberto Muñoz-Ortiz, Mark Anderson, David Vilares, Carlos Gómez-Rodríguez

PoS tags, once taken for granted as a useful resource for syntactic parsing, have become more situational with the popularization of deep learning.

POS

The Fragility of Multi-Treebank Parsing Evaluation

1 code implementation COLING 2022 Iago Alonso-Alonso, David Vilares, Carlos Gómez-Rodríguez

Treebank selection for parsing evaluation and the spurious effects that might arise from a biased choice have not been explored in detail.

LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing

no code implementations SemEval (NAACL) 2022 Iago Alonso-Alonso, David Vilares, Carlos Gómez-Rodríguez

This paper addressed the problem of structured sentiment analysis using a bi-affine semantic dependency parser, large pre-trained language models, and publicly available translation models.

Dependency Parsing Semantic Dependency Parsing +2

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

no code implementations RANLP 2021 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 the token arcs as bracket strings, or (iii) associating partial transition sequences of a transition-based parser to words.

Dependency Parsing

Bertinho: Galician BERT Representations

no code implementations25 Mar 2021 David Vilares, Marcos Garcia, Carlos Gómez-Rodríguez

The experiments show that our models, especially the 12-layer one, outperform the results of mBERT in most tasks.

Dependency Parsing named-entity-recognition +4

Bracketing Encodings for 2-Planar Dependency Parsing

1 code implementation COLING 2020 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 total coverage of crossing arcs in sequence labeling parsing.

Dependency Parsing POS +1

A Unifying Theory of Transition-based and Sequence Labeling Parsing

1 code implementation COLING 2020 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.

Dependency Parsing

Discontinuous Constituent Parsing as Sequence Labeling

1 code implementation EMNLP 2020 David Vilares, Carlos Gómez-Rodríguez

Second, it fills this gap and proposes to encode tree discontinuities as nearly ordered permutations of the input sequence.

Towards Making a Dependency Parser See

1 code implementation IJCNLP 2019 Michalina Strzyz, David Vilares, Carlos Gómez-Rodríguez

We explore whether it is possible to leverage eye-tracking data in an RNN dependency parser (for English) when such information is only available during training, i. e., no aggregated or token-level gaze features are used at inference time.

Artificially Evolved Chunks for Morphosyntactic Analysis

no code implementations WS 2019 Mark Anderson, David Vilares, Carlos Gómez-Rodríguez

We introduce a language-agnostic evolutionary technique for automatically extracting chunks from dependency treebanks.

Chunking Dependency Parsing +2

Sequence Labeling Parsing by Learning Across Representations

1 code implementation ACL 2019 Michalina Strzyz, David Vilares, Carlos Gómez-Rodríguez

We use parsing as sequence labeling as a common framework to learn across constituency and dependency syntactic abstractions.

Constituency Parsing Dependency Parsing

Harry Potter and the Action Prediction Challenge from Natural Language

1 code implementation NAACL 2019 David Vilares, Carlos Gómez-Rodríguez

We explore the challenge of action prediction from textual descriptions of scenes, a testbed to approximate whether text inference can be used to predict upcoming actions.

Better, Faster, Stronger Sequence Tagging Constituent Parsers

2 code implementations NAACL 2019 David Vilares, Mostafa Abdou, Anders Søgaard

Combining these techniques, we clearly surpass the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebanks, and reduce their parsing time even further.

Multi-Task Learning Sentence

Viable Dependency Parsing as Sequence Labeling

1 code implementation NAACL 2019 Michalina Strzyz, David Vilares, Carlos Gómez-Rodríguez

We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels.

Dependency Parsing

Constituent Parsing as Sequence Labeling

1 code implementation EMNLP 2018 Carlos Gómez-Rodríguez, David Vilares

For each word w_t, it generates a label that encodes: (1) the number of ancestors in the tree that the words w_t and w_{t+1} have in common, and (2) the nonterminal symbol at the lowest common ancestor.

Grounding the Semantics of Part-of-Day Nouns Worldwide using Twitter

1 code implementation WS 2018 David Vilares, Carlos Gómez-Rodríguez

The usage of part-of-day nouns, such as 'night', and their time-specific greetings ('good night'), varies across languages and cultures.

Detecting Perspectives in Political Debates

1 code implementation EMNLP 2017 David Vilares, Yulan He

We explore how to detect people{'}s perspectives that occupy a certain proposition.

Topic Models

Towards Syntactic Iberian Polarity Classification

1 code implementation WS 2017 David Vilares, Marcos Garcia, Miguel A. Alonso, Carlos Gómez-Rodríguez

Lexicon-based methods using syntactic rules for polarity classification rely on parsers that are dependent on the language and on treebank guidelines.

Classification General Classification

How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis

no code implementations7 Jun 2017 Carlos Gómez-Rodríguez, Iago Alonso-Alonso, David Vilares

Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech.

Sentiment Analysis

Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis

no code implementations17 Jun 2016 David Vilares, Carlos Gómez-Rodríguez, Miguel A. Alonso

We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules.

Sentiment Analysis

One model, two languages: training bilingual parsers with harmonized treebanks

no code implementations ACL 2016 David Vilares, Carlos Gómez-Rodríguez, Miguel A. Alonso

We introduce an approach to train lexicalized parsers using bilingual corpora obtained by merging harmonized treebanks of different languages, producing parsers that can analyze sentences in either of the learned languages, or even sentences that mix both.

Vocal Bursts Valence Prediction

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