Ivan Meza-Ruiz

Also published as: Ivan Meza, Ivan V. Meza, Ivan Vladimir Meza-Ruiz, Ivan Vladimir Meza Ruiz


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UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones
Ignacio Arroyo-Fernández | Ivan Meza | Carlos-Francisco Méndez-Cruz
Proceedings of The 12th International Workshop on Semantic Evaluation

In this paper we report an unsupervised method aimed to identify whether an attribute is discriminative for two words (which are treated as concepts, in our particular case). To this end, we use geometrically inspired vector operations underlying unsupervised decision functions. These decision functions operate on state-of-the-art neural word embeddings of the attribute and the concepts. The main idea can be described as follows: if attribute q discriminates concept a from concept b, then q is excluded from the feature set shared by these two concepts: the intersection. That is, the membership q∈ (a∩ b) does not hold. As a,b,q are represented with neural word embeddings, we tested vector operations allowing us to measure membership, i.e. fuzzy set operations (t-norm, for fuzzy intersection, and t-conorm, for fuzzy union) and the similarity between q and the convex cone described by a and b.

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Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages
Katharina Kann | Jesus Manuel Mager Hois | Ivan Vladimir Meza-Ruiz | Hinrich Schütze
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostly) European languages, we first show that they also obtain competitive performance for Mexican polysynthetic languages in minimal-resource settings. We then propose two novel multi-task training approaches—one with, one without need for external unlabeled resources—, and two corresponding data augmentation methods, improving over the neural baseline for all languages. Finally, we explore cross-lingual transfer as a third way to fortify our neural model and show that we can train one single multi-lingual model for related languages while maintaining comparable or even improved performance, thus reducing the amount of parameters by close to 75%. We provide our morphological segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for future research.

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Challenges of language technologies for the indigenous languages of the Americas
Manuel Mager | Ximena Gutierrez-Vasques | Gerardo Sierra | Ivan Meza-Ruiz
Proceedings of the 27th International Conference on Computational Linguistics

Indigenous languages of the American continent are highly diverse. However, they have received little attention from the technological perspective. In this paper, we review the research, the digital resources and the available NLP systems that focus on these languages. We present the main challenges and research questions that arise when distant languages and low-resource scenarios are faced. We would like to encourage NLP research in linguistically rich and diverse areas like the Americas.

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Lost in Translation: Analysis of Information Loss During Machine Translation Between Polysynthetic and Fusional Languages
Manuel Mager | Elisabeth Mager | Alfonso Medina-Urrea | Ivan Vladimir Meza Ruiz | Katharina Kann
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

Machine translation from polysynthetic to fusional languages is a challenging task, which gets further complicated by the limited amount of parallel text available. Thus, translation performance is far from the state of the art for high-resource and more intensively studied language pairs. To shed light on the phenomena which hamper automatic translation to and from polysynthetic languages, we study translations from three low-resource, polysynthetic languages (Nahuatl, Wixarika and Yorem Nokki) into Spanish and vice versa. Doing so, we find that in a morpheme-to-morpheme alignment an important amount of information contained in polysynthetic morphemes has no Spanish counterpart, and its translation is often omitted. We further conduct a qualitative analysis and, thus, identify morpheme types that are commonly hard to align or ignored in the translation process.


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LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity
Ignacio Arroyo-Fernández | Ivan Vladimir Meza Ruiz
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper we report our attempt to use, on the one hand, state-of-the-art neural approaches that are proposed to measure Semantic Textual Similarity (STS). On the other hand, we propose an unsupervised cross-word alignment approach, which is linguistically motivated. The neural approaches proposed herein are divided into two main stages. The first stage deals with constructing neural word embeddings, the components of sentence embeddings. The second stage deals with constructing a semantic similarity function relating pairs of sentence embeddings. Unfortunately our competition results were poor in all tracks, therefore we concentrated our research to improve them for Track 5 (EN-EN).


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LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies
Oscar William Lightgow Serrano | Ivan Vladimir Meza Ruiz | Albert Manuel Orozco Camacho | Jorge Garcia Flores | Davide Buscaldi
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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SOPA: Random Forests Regression for the Semantic Textual Similarity task
Davide Buscaldi | Jorge García Flores | Ivan V. Meza | Isaac Rodríguez
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)


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Multilingual Semantic Role Labelling with Markov Logic
Ivan Meza-Ruiz | Sebastian Riedel
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task

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Jointly Identifying Predicates, Arguments and Senses using Markov Logic
Ivan Meza-Ruiz | Sebastian Riedel
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics


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Collective Semantic Role Labelling with Markov Logic
Sebastian Riedel | Ivan Meza-Ruiz
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning


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Multi-lingual Dependency Parsing with Incremental Integer Linear Programming
Sebastian Riedel | Ruket Çakıcı | Ivan Meza-Ruiz
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)