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Semantic relation extraction from unstructured data in the business domain
Rampula, Ilana ; Pecina, Pavel (advisor) ; Kuboň, Vladislav (referee)
Text analytics in the business domain is a growing field in research and practical applications. We chose to concentrate on Relation Extraction from unstructured data which was provided by a corporate partner. Analyzing text from this domain requires a different approach, counting with irregularities and domain specific attributes. In this thesis, we present two methods for relation extraction. The Snowball system and the Distant Supervision method were both adapted for the unique data. The methods were implemented to use both structured and unstructured data from the database of the company. Keywords: Information Retrieval, Relation Extraction, Text Analytics, Distant Supervision, Snowball
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Relation extraction in police records
Ejem, Richard ; Žabokrtský, Zdeněk (advisor) ; Mareček, David (referee)
This work describes a problem of relation extraction between named entities on the sentence level, assuming that the named entities are already tagged in the text, on the domain of police reports written by the Anti-drug Department of the Police of the Czech Republic. We have used various methods of machine learning in combination with tree kernel functions and methods based on sentence syntax rules. None of the used methods had satisfying results on the data provided by the Police of the Czech Republic. Following analysis showed that tagging of the relations in the data was missing many relations, which were obvious to a human reader. That was found to be the reason why the supervised machine learning was not successful. Later in this work we present several rules for recognizing relations which we have identified manually. Findings in this work may be helpful for future research of processing these police reports.
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Semantic relation extraction from unstructured data in the business domain
Rampula, Ilana ; Pecina, Pavel (advisor) ; Kuboň, Vladislav (referee)
Text analytics in the business domain is a growing field in research and practical applications. We chose to concentrate on Relation Extraction from unstructured data which was provided by a corporate partner. Analyzing text from this domain requires a different approach, counting with irregularities and domain specific attributes. In this thesis, we present two methods for relation extraction. The Snowball system and the Distant Supervision method were both adapted for the unique data. The methods were implemented to use both structured and unstructured data from the database of the company. Keywords: Information Retrieval, Relation Extraction, Text Analytics, Distant Supervision, Snowball
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