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Catching criminals by chance: a probabilistic approach to named entity recognition using targeted feedback

Kuperus, Jasper (2012) Catching criminals by chance: a probabilistic approach to named entity recognition using targeted feedback.

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Abstract:In forensics, large amounts of unstructured data have to be analyzed in order to find evidence or to detect risks. For example, the contents of a personal computer or USB data carriers belonging to a suspect. Automatic processing of these large amounts of unstructured data, using techniques like Information Extraction, is inevitable. Named Entity Recognition (NER) is an important first step in Information Extraction and still a diffcult task. A main challenge in NER is the ambiguity among the extracted named entities. Most approaches take a hard decision on which named entities belong to which class or which boundary fits an entity. However, often there is a significant amount of ambiguity when making this choice, resulting in errors by making these hard decisions. Instead of making such a choice, all possible alternatives can be preserved with a corresponding confidence of the probability that it is the correct choice. Extracting and handling entities in such a probabilistic way is called Probabilistic Named Entity Recognition (PNER). Combining the fields of Probabilistic Databases and Information Extraction results in a new field of research. This research project explores the problem of Probabilistic NER. Although Probabilistic NER does not make hard decisions when ambiguity is involved, it also does not yet resolve ambiguity. A way of resolving this ambiguity is by using user feedback to let the probabilities converge to the real world situation, called Targeted Feedback. The main goal in this project is to improve NER results by using PNER, preventing ambiguity related extraction errors and using Targeted Feedback to reduce ambiguity. This research project shows that Recall values of the PNER results are significantly higher than for regular NER, adding up to improvements over 29%. Using Targeted Feedback, both Precision and Recall approach 100% after full user feedback. For Targeted Feedback, both the order in which questions are posed and whether a strategy attempts to learn from the answers of the user provide performance gains. Although PNER shows to have potential, this research project provides insufficient evidence whether PNER is better than regular NER.
Item Type:Essay (Master)
Clients:
Netherlands Forensic Institute, Kecida
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
Link to this item:http://purl.utwente.nl/essays/61639
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