In this paper, experiments on automatic extraction of keywords from abstracts using a supervised machine learning algorithm are discussed. The main point of this paper is that by adding linguistic knowledge to the representation (such as syntactic features), rather than relying only on statistics (such as term frequency and ngrams), a better result is obtained as measured by keywords previously assigned by professional indexers. In more detail, extracting NP-chunks gives a better precision than n-grams, and by adding the POS tag(s) assigned to the term as a feature, a dramatic improvement of the results is obtained, independent of the term selection approach applied.
REF reported that adding certain linguistic knowledge (e.g., syntactic features) to the training set improves performance of the automatic keyword extractor as compared to relying only on statistics-based features such as, term frequency, n-grams, etc.
Improved Automatic Keyword Extraction Given More Linguistic Knowledge
Conference On Empirical Methods In Natural Language Processing
Computer Science