Shuji Kaneko¹, Nobuyuki Fujita², Hiroshi Ohtake³ 1Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto, Japan ²National Institute of Technology and Evaluation, Tokyo, Japan ³Center for Arts and Sciences, Fukui Prefectural University, Fukui, Japan About us: Our understanding of the cellular and molecular mechanisms underlying neural function and
. The Life Science Dictionary (LSD) project, founded in 1993, is a research project to develop a systematic database for life science (of course, including medical) terms and tools for the convenience of life scientists. . Our services are designed to provide and encourage access within the scientific community to the most up-to-date and comprehensive information on English- Japanese translation dictionary of life science terms. . In keeping with the users' expectations, we have been enriching and refining the database records to a medical thesaurus compatible with MeSH (Medical Subject Headings developed by National Library of Medicine, USA) thesaurus. . Recent 2013 version of LSD contains approximately 30 thousand headings with 200 thousand English and Japanese synonyms, consisting of the names of anatomical concepts, biological organisms chemical compounds, methods, disease and symptoms.
. . One of the practical applications of thesaurus is text mining . For example, adverse drug events can be rapidly extracted by finding the causal relationship of drug treatment and related symptoms recorded in medical records. Ex. Allergy + An antihistamine → arrhythmia = a side effect of the antihystamine Favorably, we have previously developed a series of gloss-embedding Perl scripts for medical English texts. . . In this study, therefore we aimed to find every medical term (English or Japanese) as many as possible. . As the source text, an NTCIR10 test set (1,121 sentences) was used.
. A tagger dictionary was made from LSD database as an EUC text file, which contains approximately 200,000 rows and 4 columns: (1) synonym strings (2) subject heading strings (converged to 30,000 descroptors) (3) category of term (4) subject heading ID (from MeSH) for external reference link For the category of terms, all terms were classified and marked by one of the following categories according to the MeSH tree: anatomy, biological, disease, molecule, method, and knowledge. .
•   To take full advantage of the LSD in which many phrases have been registered, “the longest matches first” principle was adopted in the matching process. . For this purpose, the tagger dictionary was sorted in the descending order of byte lengths, and text matching was performed for each of the dictionary entries in this order. . Both the text and the dictionary were first converted to EUC, and they were treated as byte strings in the matching process. • All two-byte roman characters were converted to corresponding ASCII characters. . For better readability of result, a standard HTML format was used, in which “class” attribute was assigned to each of the category.

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. We also added a ‘mouse-over heading’ feature, in which the embedded subject heading of the term will be displayed when the cursor was placed over the tagged term. • In addition, by clicking the tagged part, the user can confirm the thesaurus entry in our WebLSD online . This allows users to judge the justness of tagged terms. click
. In addition to many correctly-tagged terms, several patterns of missed or incorrect tags were found. . The mostly missed terms were English abbreviations . . Especially, in the description of clinical test data, a variety of abbreviations and acronyms were used, which cannot be marked. . Since the meanings of 2- or 3-word abbreviations are ambiguous, we had omitted most of the abbreviations from tagger dictionary. . However, if we know the part of document is apparently indicating clinical data, we can make a specific tagger dictionary for clinical tests.
Table 2  List of missed abbreviations
Subcategory   Examples

. . The most typical pattern of incorrect tag was ‘partly-tagged’ term. In these cases, part of unit concepts were registered in the dictionary. . However, the combination of two or more concepts is common particularly in the names of disease and symptom, which were not completely covered in our thesaurus. . In these cases, we have to expand our dictionary. Table 3  Examples of partly-tagged words
• To our surprise, there were many misspellings and typographical errors, even in Japanese terms, in the test document. • Precise text matching did not tag incorrect spellings that medical doctor can recognize their meanings. Table 4  List of misspellings In the text (Wrong) Correct
• With our tagging dictionary and Perl scripts, most of medical terms were easily marked and visualized as an HTML document. From the 0.1 MB test document, 2,569 terms (including English spellings) • were tagged and visualized in a color HTML format. •   Additional ‘mouse-over heading’ and web reference enables easy reviewing of the tagged terms. . Through this task, we have learnt the potential and limitation of our thesaurus and scripts in finding medical terms from given Japanese texts. This process has a limitation in assigning ambiguous abbreviations and • misspelled words. •   Moreover, there is an insurmountable difficulty to accomplish a ‘perfect matching’ with a fixed text dictionary, since improvement of thesaurus is a laborious work. . The simple tagging strategy might be useful in preprocessing of medical reports. •   Combination of natural text processing with this tool will be convenient for the practical use.