Comparing Medline citations using modified N-grams
نویسندگان
چکیده
OBJECTIVE We aim to identify duplicate pairs of Medline citations, particularly when the documents are not identical but contain similar information. MATERIALS AND METHODS Duplicate pairs of citations are identified by comparing word n-grams in pairs of documents. N-grams are modified using two approaches which take account of the fact that the document may have been altered. These are: (1) deletion, an item in the n-gram is removed; and (2) substitution, an item in the n-gram is substituted with a similar term obtained from the Unified Medical Language System Metathesaurus. N-grams are also weighted using a score derived from a language model. Evaluation is carried out using a set of 520 Medline citation pairs, including a set of 260 manually verified duplicate pairs obtained from the Deja Vu database. RESULTS The approach accurately detects duplicate Medline document pairs with an F1 measure score of 0.99. Allowing for word deletions and substitution improves performance. The best results are obtained by combining scores for n-grams of length 1-5 words. DISCUSSION Results show that the detection of duplicate Medline citations can be improved by modifying n-grams and that high performance can also be obtained using only unigrams (F1=0.959), particularly when allowing for substitutions of alternative phrases.
منابع مشابه
Sentiment Analysis of Citations using Sentence Structure-Based Features
Sentiment analysis of citations in scientific papers and articles is a new and interesting problem due to the many linguistic differences between scientific texts and other genres. In this paper, we focus on the problem of automatic identification of positive and negative sentiment polarity in citations to scientific papers. Using a newly constructed annotated citation sentiment corpus, we expl...
متن کاملApplying MetaMap to Medline for identifying novel associations in a large clinical dataset: a feasibility analysis
OBJECTIVE We describe experiments designed to determine the feasibility of distinguishing known from novel associations based on a clinical dataset comprised of International Classification of Disease, V.9 (ICD-9) codes from 1.6 million patients by comparing them to associations of ICD-9 codes derived from 20.5 million Medline citations processed using MetaMap. Associations appearing only in th...
متن کاملCitations in the Digital Library of Classics: Extracting Canonical References by Using Conditional Random Fields
Scholars of Classics cite ancient texts by using abridged citations called canonical references. In the scholarly digital library, canonical references create a complex textile of links between ancient and modern sources reflecting the deep hypertextual nature of texts in this field. This paper aims to demonstrate the suitability of Conditional Random Fields (CRF) for extracting this particular...
متن کاملGenerating the MEDLINE N-Gram Set
The MEDLINE n-gram set is a very useful resource in Natural Language Processing (NLP) and Medical Language Processing (MLP). Currently, there is no MEDLINE n-gram set available in the public domain. Due to the large scale of data, it is a challenge to generate MEDLINE n-grams to fit into a research schedule with limited computer resources. The Lexical System Group (LSG) developed an algorithm t...
متن کاملComparing word, character, and phoneme n-grams for subjective utterance recognition
In this paper, we compare the performance of classifiers trained using word n-grams, character n-grams, and phoneme n-grams for recognizing subjective utterances in multiparty conversation. We show that there is value in using very shallow linguistic representations, such as character n-grams, for recognizing subjective utterances, in particular, gains in the recall of subjective utterances.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Journal of the American Medical Informatics Association : JAMIA
دوره 21 1 شماره
صفحات -
تاریخ انتشار 2014