Automatic Labelling of Topic Models Using Word Vectors and Letter Trigram Vectors
نویسندگان
چکیده
The native representation of LDA-style topics is a multinomial distributions over words, but automatic labelling of such topics has been shown to help readers interpret the topics better. We propose a novel framework for topic labelling using word vectors and letter trigram vectors. We generate labels automatically and propose automatic and human evaluations of our method. First, we use a chunk parser to generate candidate labels, then map topics and candidate labels to word vectors and letter trigram vectors in order to find which candidate label are more semantically related with that topic. A label can be found by calculating the similarity between a topic and its candidate label vectors. Experiments on three data sets show that the method is effective.
منابع مشابه
Kou, Wanqiu, Li Fang and Timothy Baldwin (to appear) Automatic Labelling of Topic Models using Word Vectors and Letter Trigram Vectors, in Proceedings of the Eleventh Asian Information Retrieval Societies Conference (AIRS 2015), Brisbane, Australia
The native representation of LDA-style topics is a multinomial distributions over words, which can be time-consuming to interpret directly. As an alternative representation, automatic labelling has been shown to help readers interpret the topics more efficiently. We propose a novel framework for topic labelling using word vectors and letter trigram vectors. We generate labels automatically and ...
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