Michal Konkol


2018

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UWB at SemEval-2018 Task 10: Capturing Discriminative Attributes from Word Distributions
Tomáš Brychcín | Tomáš Hercig | Josef Steinberger | Michal Konkol
Proceedings of The 12th International Workshop on Semantic Evaluation

We present our UWB system for the task of capturing discriminative attributes at SemEval 2018. Given two words and an attribute, the system decides, whether this attribute is discriminative between the words or not. Assuming Distributional Hypothesis, i.e., a word meaning is related to the distribution across contexts, we introduce several approaches to compare word contextual information. We experiment with state-of-the-art semantic spaces and with simple co-occurrence statistics. We show the word distribution in the corpus has potential for detecting discriminative attributes. Our system achieves F1 score 72.1% and is ranked #4 among 26 submitted systems.

2017

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Geographical Evaluation of Word Embeddings
Michal Konkol | Tomáš Brychcín | Michal Nykl | Tomáš Hercig
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Word embeddings are commonly compared either with human-annotated word similarities or through improvements in natural language processing tasks. We propose a novel principle which compares the information from word embeddings with reality. We implement this principle by comparing the information in the word embeddings with geographical positions of cities. Our evaluation linearly transforms the semantic space to optimally fit the real positions of cities and measures the deviation between the position given by word embeddings and the real position. A set of well-known word embeddings with state-of-the-art results were evaluated. We also introduce a visualization that helps with error analysis.

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Joint Unsupervised Learning of Semantic Representation of Words and Roles in Dependency Trees
Michal Konkol
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

In this paper, we introduce WoRel, a model that jointly learns word embeddings and a semantic representation of word relations. The model learns from plain text sentences and their dependency parse trees. The word embeddings produced by WoRel outperform Skip-Gram and GloVe in word similarity and syntactical word analogy tasks and have comparable results on word relatedness and semantic word analogy tasks. We show that the semantic representation of relations enables us to express the meaning of phrases and is a promising research direction for semantics at the sentence level.

2016

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UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Tomáš Hercig | Tomáš Brychcín | Lukáš Svoboda | Michal Konkol
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification
Michal Konkol
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2014

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UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis
Tomáš Brychcín | Michal Konkol | Josef Steinberger
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

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Aspect-Level Sentiment Analysis in Czech
Josef Steinberger | Tomáš Brychcín | Michal Konkol
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis