Improving Keyphrase Extraction from Biomedical Documents Using Domain Specific Feature Set

Abstract — Keyphrases enable the reader to quickly determine whether the given article is suitable for the reader’s digest. Keyphrases are also important for medical document retrieval and text mining research. Sometimes, the author-assigned Keyphrases or keywords available with the articles are too limited to represent the topical content of the articles. Many medical documents also do not come with author assigned Keyphrases. So, an automatic Keyphrase extraction process is highly desirable. Domain specific features can play an important role in domain specific Keyphrase extraction. This paper presents an automatic Keyphrase extraction method that exploits a number of domain-specific features to improve the Keyphrase extraction performance in medical domain. Index Terms — domain specific Keyphrase extraction, medical documents, text mining I. I NTRODUCTION Medical Literature such as research articles, clinical trial reports, medical case reports, medical news available on the web are the important sources to help clinicians in patient care. The pervasion of huge amount of medical information through WWW has created a growing need for the development of techniques for discovering, accessing, and sharing knowledge from medical literature. The Keyphrases help readers rapidly understand, organize, access, and share information of a document. Document Keyphrases provide a concise summary of the document content. In a recent work presented in [1], Keyphrases have been incorporated in the search results as subject metadata to facilitate information search on the web. Medical research articles published in the journals generally come with several author assigned Keyphrases. But, medical articles such as medical news, case reports, medical commentaries etc. may not have author assigned Keyphrases. Sometimes, the number of author-assigned Keyphrases available with the articles is too limited to represent the topical content of the articles. So, an automatic Keyphrase extraction process is highly desirable. A number of previous works has suggested that document Keyphrases can be useful in a various applications such as retrieval engines [2], [3], browsing interfaces [4], thesaurus construction [5], and document classification and clustering [6]. Turney [7] treats the problem of Keyphrase extraction as supervised learning task. In this task, nine features are used to score a candidate phrase; some of the features are positional information of the phrase in the document and whether or not the phrase is a proper noun. Keyphrases are extracted from candidate phrases based on examination of their features. Turney’s program is called Extractor. One form of this extractor is called GenEx, which is designed based on a set of parameterized heuristic rules that are fine-tuned using a genetic algorithm. Turney Compares GenEX to the standard machine learning technique called Bagging which uses a bag of decision trees for Keyphrase extraction and shows that GenEX performs better than the bagging procedure. A Keyphrase extraction program called Kea, developed by Frank et al. [8], uses Bayesian learning for Keyphrase extraction task. A model is learned from the training documents with exemplar Keyphrases and corresponds to a specific corpus containing the training documents. Each model consists of a Naive Bayes classifier and two supporting files containing phrase frequencies and stopped words. The learned model is used to identify the Keyphrases from a document. In both Kea and Extractor, the candidate keyphrases are identified by splitting up the input text according to phrase boundaries (numbers, punctuation marks, dashes, and brackets etc.). Finally a phrase is defined as a sequence of one, two, or three words that appear consecutively in the text. The phrases beginning or ending with a stopped word are not taken under consideration. Kea and Extractor both used supervised machine learning based approaches. Two important features such as distance of the phrase's first appearance into the document and TF*IDF (used in information retrieval setting), have been considered for the development of Kea. Here TF corresponds to the frequency of the phrase into the document and IDF is estimated by counting the number of documents in the training corpus that contain the phrase P. Frank et al. [8] compares performance of the kea to Turney’s work and shows that performance of Kea is comparable to GenEx proposed by Turney. Moreover, Frank et al. [8] claims that training Naive Bayes learning technique is quicker than training GenEx, which employs the special purpose genetic algorithm for training. They suggest that deriving domain specific models would be less practical with the original lengthy genetic algorithm approach. Our work differs from the previous approach proposed by Frank et al. [8] in some ways: (1) we use POS tagger based phrase identification method, (2) we introduce a number of domain specific features and statistical