Unsupervised Keyphrase Extraction from Scientific Publications
This addresses the problem of automatically extracting keyphrases for researchers and information retrieval systems, but it is incremental as it builds on existing unsupervised techniques.
The paper tackles unsupervised keyphrase extraction from scientific publications by using outlier detection on word embeddings to filter candidate keywords, and it reports that the approach outperforms state-of-the-art and recent unsupervised methods.
We propose a novel unsupervised keyphrase extraction approach that filters candidate keywords using outlier detection. It starts by training word embeddings on the target document to capture semantic regularities among the words. It then uses the minimum covariance determinant estimator to model the distribution of non-keyphrase word vectors, under the assumption that these vectors come from the same distribution, indicative of their irrelevance to the semantics expressed by the dimensions of the learned vector representation. Candidate keyphrases only consist of words that are detected as outliers of this dominant distribution. Empirical results show that our approach outperforms state-of-the-art and recent unsupervised keyphrase extraction methods.