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This paper presents an approach for helping in the topic detection tasks. The idea is to use collocation measures to extract key terminology from plain text. We use three measures for ranking N-grams (sequence of terms), Point mutual information, Likelihood-ratio and Chisquare. With this measures we built three different groups: bigrams, trigrams and quadrigrams. Each of the measures were implemented with the purpose of comparing and helping in the detection of good key terminology in plain text. In order to obtain the best N-grams, we have implemented two filters: the first one is to get common N-grams with the highest values in the three measures (intersection). For this, we use the most significant percentages of N-grams to create subsets and then select the key terminology with high value in the three measures. The second filter is to detect the occurrence of important collocations based on part-of-speech patterns. The corpora used in this research work was obtained from the website jobs, i.e. related to job descriptions. In the results we show the key terminology extracted by this approach to demonstrate its effectiveness.

Towards Topic Detection in Plain Documents Using Key Terminology Extraction Supported by Institute of Technology Tallaght