We present a new approach to extracting keyphrases based on statistical language models. Our approach is to use pointwise KL-divergence between multiple language models for scoring both phraseness and informativeness, which can be unified into a single score to rank extracted phrases.
Also, Kea adopts machine learning techniques to A statistical language model is used by Takashi Tomokiyo and Matthew Hurst to extract keyphrases, where phraseness and informativeness unified into a single score to rank the automatically extracted keyphrases REF .
A Language Model Approach To Keyphrase Extraction
Workshop On Multiword Expressions: Analysis, Acquisition And Treatment
Computer Science