Helena Gomez

Also published as: Helena Gómez


A Parallel Corpus Mixtec-Spanish
Cynthia Montaño | Gerardo Sierra Martínez | Gemma Bel-Enguix | Helena Gomez
Proceedings of the 2019 Workshop on Widening NLP

This work is about the compilation process of parallel documents Spanish-Mixtec. There are not many Spanish-Mixec parallel texts and most of the sources are non-digital books. Due to this, we need to face the errors when digitizing the sources and difficulties in sentence alignment, as well as the fact that does not exist a standard orthography. Our parallel corpus consists of sixty texts coming from books and digital repositories. These documents belong to different domains: history, traditional stories, didactic material, recipes, ethnographical descriptions of each town and instruction manuals for disease prevention. We have classified this material in five major categories: didactic (6 texts), educative (6 texts), interpretative (7 texts), narrative (39 texts), and poetic (2 texts). The final total of tokens is 49,814 Spanish words and 47,774 Mixtec words. The texts belong to the states of Oaxaca (48 texts), Guerrero (9 texts) and Puebla (3 texts). According to this data, we see that the corpus is unbalanced in what refers to the representation of the different territories. While 55% of speakers are in Oaxaca, 80% of texts come from this region. Guerrero has the 30% of speakers and the 15% of texts and Puebla, with the 15% of the speakers has a representation of the 5% in the corpus.


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Discriminating between Similar Languages Using a Combination of Typed and Untyped Character N-grams and Words
Helena Gomez | Ilia Markov | Jorge Baptista | Grigori Sidorov | David Pinto
Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)

This paper presents the cic_ualg’s system that took part in the Discriminating between Similar Languages (DSL) shared task, held at the VarDial 2017 Workshop. This year’s task aims at identifying 14 languages across 6 language groups using a corpus of excerpts of journalistic texts. Two classification approaches were compared: a single-step (all languages) approach and a two-step (language group and then languages within the group) approach. Features exploited include lexical features (unigrams of words) and character n-grams. Besides traditional (untyped) character n-grams, we introduce typed character n-grams in the DSL task. Experiments were carried out with different feature representation methods (binary and raw term frequency), frequency threshold values, and machine-learning algorithms – Support Vector Machines (SVM) and Multinomial Naive Bayes (MNB). Our best run in the DSL task achieved 91.46% accuracy.


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CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings
Helena Gomez | Darnes Vilariño | Grigori Sidorov | David Pinto Avendaño
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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CICBUAPnlp: Graph-Based Approach for Answer Selection in Community Question Answering Task
Helena Gomez | Darnes Vilariño | David Pinto | Grigori Sidorov
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)


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BUAP: N-gram based Feature Evaluation for the Cross-Lingual Textual Entailment Task
Darnes Vilariño | David Pinto | Saúl León | Yuridiana Alemán | Helena Gómez
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)