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Full-Text Articles in Physical Sciences and Mathematics
Dynamic Generation Of A Table Of Contents With Consumer-Friendly Labels, Trudi Miller '08, Gondy Leroy, Elizabeth Wood
Dynamic Generation Of A Table Of Contents With Consumer-Friendly Labels, Trudi Miller '08, Gondy Leroy, Elizabeth Wood
CGU Faculty Publications and Research
Consumers increasingly look to the Internet for health information, but available resources are too difficult for the majority to understand. Interactive tables of contents (TOC) can help consumers access health information by providing an easy to understand structure. Using natural language processing and the Unified Medical Language System (UMLS), we have automatically generated TOCs for consumer health information. The TOC are categorized according to consumer-friendly labels for the UMLS semantic types and semantic groups. Categorizing phrases by semantic types is significantly more correct and relevant. Greater correctness and relevance was achieved with documents that are difficult to read than with …
Health Information Text Characteristics, Gondy Leroy, Evren Eryilmaz '11, Benjamin T. Laroya
Health Information Text Characteristics, Gondy Leroy, Evren Eryilmaz '11, Benjamin T. Laroya
CGU Faculty Publications and Research
Millions of people search online for medical text, but these texts are often too complicated to understand. Readability evaluations are mostly based on surface metrics such as character or words counts and sentence syntax, but content is ignored. We compared four types of documents, easy and difficult WebMD documents, patient blogs, and patient educational material, for surface and content-based metrics. The documents differed significantly in reading grade levels and vocabulary used. WebMD pages with high readability also used terminology that was more consumer-friendly. Moreover, difficult documents are harder to understand due to their grammar and word choice and because they …
Effects Of Information And Machine Learning Algorithms On Word Sense Disambiguation With Small Datasets, Gondy Leroy, Thomas C. Rindflesch
Effects Of Information And Machine Learning Algorithms On Word Sense Disambiguation With Small Datasets, Gondy Leroy, Thomas C. Rindflesch
CGU Faculty Publications and Research
Current approaches to word sense disambiguation use (and often combine) various machine learning techniques. Most refer to characteristics of the ambiguity and its surrounding words and are based on thousands of examples. Unfortunately, developing large training sets is burdensome, and in response to this challenge, we investigate the use of symbolic knowledge for small datasets. A naïve Bayes classifier was trained for 15 words with 100 examples for each. Unified Medical Language System (UMLS) semantic types assigned to concepts found in the sentence and relationships between these semantic types form the knowledge base. The most frequent sense of a word …
Using Symbolic Knowledge In The Umls To Disambiguate Words In Small Datasets With A Naive Bayes Classifier, Gondy Leroy, Thomas C. Rindflesch
Using Symbolic Knowledge In The Umls To Disambiguate Words In Small Datasets With A Naive Bayes Classifier, Gondy Leroy, Thomas C. Rindflesch
CGU Faculty Publications and Research
Current approaches to word sense disambiguation use and combine various machine-learning techniques. Most refer to characteristics of the ambiguous word and surrounding words and are based on hundreds of examples. Unfortunately, developing large training sets is time-consuming. We investigate the use of symbolic knowledge to augment machine-learning techniques for small datasets. UMLS semantic types assigned to concepts found in the sentence and relationships between these semantic types form the knowledge base. A naïve Bayes classifier was trained for 15 words with 100 examples for each. The most frequent sense of a word served as the baseline. The effect of increasingly …
Medtextus: An Ontology-Enhanced Medical Portal, Gondy Leroy, Hsinchun Chen
Medtextus: An Ontology-Enhanced Medical Portal, Gondy Leroy, Hsinchun Chen
CGU Faculty Publications and Research
In this paper we describe MedTextus, an online medical search portal with dynamic search and browse tools. To search for information, MedTextus lets users request synonyms and related terms specifically tailored to their query. A mapping algorithm dynamically builds the query context based on the UMLS ontology and then selects thesaurus terms that fit this context. Users can add these terms to their query and meta-search five medical databases. To facilitate browsing, the search results can be reviewed as a list of documents per database, as a set of folders into which all the documents are automatically categorized based on …
Customizable And Ontology-Enhanced Medical Information Retrieval Interfaces, Gondy Leroy, K.M. Tolle, Hsinchun Chen
Customizable And Ontology-Enhanced Medical Information Retrieval Interfaces, Gondy Leroy, K.M. Tolle, Hsinchun Chen
CGU Faculty Publications and Research
This paper describes the development and testing of the Medical Concept Mapper as an aid to providing synonyms and semantically related concepts to improve searching. All terms are related to the userquery and fit into the query context. The system is unique because its five components combine humancreated and computer-generated elements. The Arizona Noun Phraser extracts phrases from natural language user queries. WordNet and the UMLS Metathesaurus provide synonyms. The Arizona Concept Space generates conceptually related terms. Semantic relationships between queries and concepts are established using the UMLS Semantic Net. Two user studies conducted to evaluate the system are described.