MSc Mika Timonen will defend his doctoral thesis Term Weighting in Short Documents for Document Categorization, Keyword Extraction and Query Expansion on Friday 25th of January, 2013 at noon in the University of Helsinki Main Building, Unioninkatu 34, Auditorium XIII (old part), 3rd floor. His opponent is Docent Timo Honkela (Aalto University) and custos Professor Hannu Toivonen (University of Helsinki). The defense will be held in Finnish.
Term Weighting in Short Documents for Document Categorization, Keyword Extraction and Query Expansion
This thesis focuses on term weighting in short documents. I propose weighting approaches for assessing the importance of terms for three tasks: (1) document categorization, which aims to classify documents such as tweets into categories, (2) keyword extraction, which aims to identify and extract the most important words of a document, and (3) keyword association modeling, which aims to identify links between keywords and use them for query expansion.
As the focus of text mining is shifting toward datasets that hold user-generated content, for example, social media, the type of data used in the text mining research is changing. The main characteristic of this data is its shortness. For example, a user status update usually contains less than 20 words.
When using short documents, the biggest challenge in term weighting comes from the fact that most words of a document occur only once within the document. This is called hapax legomena and we call it Term Frequency = 1, or TF=1 challenge. As many traditional feature weighting approaches, such as Term Frequency - Inverse Document Frequency, are based on the occurrence frequency of each word within a document, these approaches do not perform well with short documents.
The first contribution of this thesis is a term weighting approach for document categorization. This approach is directed to combat the TF=1 challenge by excluding the traditional term frequency from the weighting method. It is replaced by using word distribution among categories and within a single category as the main components.
The second contribution of this thesis is a keyword extraction approach that uses three levels of word evaluation: corpus level, cluster level, and document level. I propose novel weighting approaches for all of these levels. This approach is designed to be used with short documents.
Finally, the third contribution of this thesis is an approach for keyword association weighting that is used for query expansion. This approach uses keyword co-occurrences as the main component and creates an association network that aims to identify strong links between the keywords.
The main finding of this study is that the existing term weighting approaches have trouble performing well with short documents. The novel algorithms proposed in this thesis produce promising results both for the keyword extraction and for the text categorization. In addition, when using keyword weighting with query expansion, we show that we are able to produce better search results especially when the original search terms would not produce any results.
Availability of the dissertation
An electronic version of the doctoral dissertation is available on the e-thesis site of the University of Helsinki at http://urn.fi/URN:ISBN:978-952-10-8567-3.
Printed copies are available on request from Mika Timonen: 050-5940002 or mika.timonen@vtt.fi.