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Artificial Neural Network for Opinion Target Identification in Czech
Glončák, Vladan ; Kuboň, Vladislav (advisor) ; Mírovský, Jiří (referee)
The main focus of this thesis is to use neural networks, specifically long short-term memory cells, for identifying opinion targets in Czech data. The side product is a new version of dataset for opinion target identification. For a comparison, previously obtained results for another languages and by employing probabilistic methods instead were listed. The experiment was successful, achieved results are above trivial baseline models and comparable with the results achieved previously. Powered by TCPDF (www.tcpdf.org)
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Využití syntaktické informace pro identifikaci hodnocených entit
Glončák, Vladan ; Hajič, Jan (advisor) ; Helcl, Jindřich (referee)
Opinion Target Extraction (OTE) is a well-established subtask of sentiment analysis. While detecting sentiment polarity is useful in itself, the ability to extract the targets of the opinions allows for more thorough decision making. For example, an owner of a restaurant needs to know whether the guests are complaining about the food, or the ambience, or any other aspect of their establishment, etc. Despite the lexical information being crucial for the task, syntactic structures have potential in being used to correctly decide among multiple candidate entities. Rules based on such structures have been used previously for the task. The objective of this thesis is to investigate, whether syntactic information influences the behavior of the state-of-the-art models such as recurrent neural networks for the OTE task. We did not find any substantial evidence to suggest that adding the syntactic information influences the behavior of the models.
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Artificial Neural Network for Opinion Target Identification in Czech
Glončák, Vladan ; Kuboň, Vladislav (advisor) ; Mírovský, Jiří (referee)
The main focus of this thesis is to use neural networks, specifically long short-term memory cells, for identifying opinion targets in Czech data. The side product is a new version of dataset for opinion target identification. For a comparison, previously obtained results for another languages and by employing probabilistic methods instead were listed. The experiment was successful, achieved results are above trivial baseline models and comparable with the results achieved previously. Powered by TCPDF (www.tcpdf.org)
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