Andreas Weiermann See discussions, stats, and author profiles for this publication at:
Abstract Background Identification of terms is essential for biomedical text mining. To make the UMLS more suitable for biomedical text mining we implemented and evaluated nine term rewrite and eight term suppression rules.
The rules rely on UMLS properties that have been identified in previous work by others, together with an additional set of new properties discovered by our group during our work with the UMLS. Our work complements the earlier work in that we measure the impact on the number of terms identified by the different rules on a MEDLINE corpus.
The 50 most frequently found terms together with a sample of randomly selected terms were evaluated for every rule. Results Five of the nine rewrite rules were found to generate additional synonyms and spelling variants that correctly corresponded to the meaning of the original terms and seven out of the eight suppression rules were found to suppress only undesired terms.
Using the five rewrite rules that passed our evaluation, we were able to identify 1, new occurrences of 14, rewritten terms in MEDLINE. Without the rewriting, we recognizedterms belonging toconcepts; with rewriting, we recognizedterms belonging toconcepts, which is an increase of 2.
Using the seven suppression rules, a total ofundesired terms were suppressed in the UMLS, notably decreasing its size. A software tool to apply these rules to the UMLS is freely available at http: Background Biomedical text mining has been shown to be valuable for diverse applications in the domains of molecular biology, toxicogenomics, and medicine.
For example, it has been used to functionally annotate gene lists from microarray experiments [ 1 - 4 ], create literature-based compound profiles [ 5 ], generate medical hypotheses [ 6 term rewriting and all that pdf, 7 ], find new uses for old drugs [ 8 - 10 ], and measure protein similarity [ 1112 ].
The identification of biomedical terms in natural language is essential for biomedical text mining. The process of term identification consists of three tasks: Approaches to term identification generally fall into three categories: All approaches have their disadvantages: Term mapping, in which terms are linked to reference data sources, is the last step in the term identification process.
Term mapping is only possible using lexicon-based term identification and is the focus of this paper for comprehensive reviews on term identification see for example [ 13 - 17 ]. In addition, the lexicon-based approach deals with general medical terms for which it is difficult to design general matching patterns that are used by rule-based systems.
It provides information concerning the semantic relations between terms and supports synonym and referent data source mapping, which is not possible using rule-based or statistically-based term identification.
These vocabularies cover different aspects of the biomedical field and have been developed for such different purposes as disease and procedure coding, adverse event reporting, literature indexing, billing, and gene function identification.
Naturally, the usefulness of the lexicon-based approach depends on the coverage of terms in the vocabulary for the particular domain and how well the terms are suited for natural language processing.
The UMLS is not primarily intended as a resource for text mining, so not all of its terms are suitable for this purpose. For example, terms for coding of concepts can include specialized syntax e.
In the Metathesaurus, a term is the class of all strings that are lexical variants made singular and normalized to case of each other". In fact, the UMLS abounds of expressions that are not expected to occur in any written or oral communication but are intended to precisely paraphrase the exact meaning of a concept.
This has been illustrated by, for example, Srinivasan et al. The lower match result in comparison with Srinivasan et al.
The investigation resulted in a number of properties that could be used to filter unwanted strings from the UMLS. Rogers and Aronson [ 26 ] identified a number of filtering rules and term types which help in filtering the UMLS for the update of the MetaMap program [ 27 ].
This paper is inspired by McCray et al. We do this by removing and adding synonyms to the UMLS, which are supposed to increase the accuracy and efficiency of biomedical term identification using the UMLS.
The suppression rules on the other hand were implemented to rid the UMLS of terms that are undesired when it comes to term identification either because they affect the precision of the term identification, e.
Finally, the identified rewritten terms were manually assessed for their correspondence to the original UMLS terms and the identified suppressed terms were manually assessed for their usefulness for automatic text mining purposes. A detailed description of the procedure follows.
The default settings in MetamorphoSys were used to create the UMLS subset, using the option to include all vocabularies in the English language.
Strings marked as suppressible by the NLM as well as strings longer than characters were not included in the analysis. Duplicate strings within a concept were removed by comparing strings after conversion to lower case and removal of punctuation; 2, strings remained and these are henceforth referred to as "terms".
Corpus creation All MEDLINE citations title and abstract available at the time of this study, with publication dates ranging from January to December 17, citations, of which 9, have an abstract were used as a test corpus. Creation of rules A set of nine rewrite rules and eight suppression rules were given.
A description of the rules together with motivation and differences in comparison to original source when applicable is provided below.
In order to avoid introducing duplicates and homonyms when applying the rewrite rules, a new term was not added to the concept if it could already be found among the synonyms for that concept or any other concept case insensitive matching after removal of punctuation.
We added the condition that only one such pattern of a comma followed by a space is to be found in a term for the rule to be executed. Possessives [ 26 ]:Hire a highly qualified essay writer to cater for all your content needs.
Whether you struggle to write an essay, coursework, research paper, annotated bibliography or dissertation, we’ll connect you with a screened academic . convergent term rewriting system, every term t can be reduced to a unique normal form, denoted by t↓ R.
A rule l→ r is called left-linear if l is linear, a term rewriting system is called left-linear if all. A thousand-year-old tooth has provided genetic evidence that the so-called "Taíno", the first indigenous Americans to feel the full impact of . TERM REWRITING AND ALL THAT Download Term Rewriting And All That ebook PDF or Read Online books in PDF, EPUB, and Mobi Format.
Click Download or Read Online button to TERM REWRITING AND ALL THAT book pdf for free now. This all does not sound to me as it was an active decision to create a term rewriting system but rather, Wolfram wrote down the specifications of how he thought an expression manipulator should be designed.
When we look at it now, it seems clear that it is of course a term-rewriting system, but maybe it wasn't so clear back then. Title: Higher Order Algebra Logic And Term Rewriting First International Workshop Hoa 93 Amsterdam The Netherlands September 23 24 Selected Papers Lecture Notes In Computer Science PDF .