Filling Preposition-based Templates to Capture Information from Medical Abstracts

Due to the recent explosion of information in the biomedical field, it is hard for a single researcher to review the complex network involving genes, proteins, and interactions. We are currently building GeneScene, a toolkit that will assist researchers in reviewing existing literature, and report on the first phase in our development effort: extracting the relevant information from medical abstracts. We are developing a medical parser that extracts information, fills basic prepositional-based templates, and combines the templates to capture the underlying sentence logic. We tested our parser on 50 unseen abstracts and found that it extracted 246 templates with a precision of 70%. In comparison with many other techniques, more information was extracted without sacrificing precision. Future improvement in precision will be achieved by correcting three categories of errors

MedTextus: An Ontology-enhanced Medical Portal

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 their content, and as a map that is built on the fly. We designed a user study to compare these dynamic support tools with the static query support of NLM Gateway and report on initial results for the search task. The users used NLM Gateway more effectively, but used MedTextus more efficiently and preferred its query formation tools

USER MISCONCEPTIONS OF INFORMATION RETRIEVAL SYSTEMS

In this paper, we report results of an investigation where thirty subjects were observed performing subject-based search in an online catalog system. The observations have revealed a range of misconceptions users have when performing subject-based search. We have developed a taxonomy that characterizes these misconceptions and hypotheses about the causes of the misconceptions. Directions for improving search performance are also suggested.Information Systems Working Papers Serie

Meeting Medical Terminology Needs: The Ontology-enhanced Medical Concept Mapper

This paper describes the development and testing of the Medical Concept Mapper, a tool designed to facilitate access to online medical information sources by providing users with appropriate medical search terms for their personal queries. Our system is valuable for patients whose knowledge of medical vocabularies is inadequate to find the desired information, and for medical experts who search for information outside their field of expertise. The Medical Concept Mapper maps synonyms and semantically related concepts to a user\u27s query. The system is unique because it integrates our natural language processing tool, i.e., the Arizona (AZ) Noun Phraser, with human-created ontologies, the Unified Medical Language System (UMLS) and WordNet, and our computer generated Concept Space, into one system. Our unique contribution results from combining the UMLS Semantic Net with Concept Space in our deep semantic parsing (DSP) algorithm. This algorithm establishes a medical query context based on the UMLS Semantic Net, which allows Concept Space terms to be filtered so as to isolate related terms relevant to the query. We performed two user studies in which Medical Concept Mapper terms were compared against human experts\u27 terms. We conclude that the AZ Noun Phraser is well suited to extract medical phrases from user queries, that WordNet is not well suited to provide strictly medical synonyms, that the UMLS Metathesaurus is well suited to provide medical synonyms, and that Concept Space is well suited to provide related medical s, especially when these terms are limited by our DSP algorithm

Sentiment analysis on Chinese web forums using elastic nets: Features, classification and interpretation: Working paper series--11-11

Consumer opinion has always been of great concern for businesses and others in the commercial sector. Among all social media which contain opinion-rich content, Web forums have become influential due to the large volume of discussions and high levels of interactivity. The Chinese market has now emerged as one of the largest ones over the world, therefore understanding the opinions and sentiments expressed by Chinese consumers has become increasingly important. In this study, we proposed a generic framework to analyze sentiment in Chinese Web forums. To detect online sentiment, we developed a classification method using Elastic Nets with rich feature representation. The proposed sentiment analysis framework was evaluated on two of the most famous Chinese forums with topics on Chinese stock market and laptop. Findings about interesting features were discussed

Exploiting Emotions in Social Interactions to Detect Online Social Communities

The rapid development of Web 2.0 allows people to be involved in online interactions more easily than before and facilitates the formation of virtual communities. Online communities exert influence on their members’ online and offline behaviors. Therefore, they are of increasing interest to researchers and business managers. Most virtual community studies consider subjects in the same Web application belong to one community. This boundary-defining method neglects subtle opinion differences among participants with similar interests. It is necessary to unveil the community structure of online participants to overcome this limitation. Previous community detection studies usually account for the structural factor of social networks to build their models. Based on the affect theory of social exchange, this research argues that emotions involved in social interactions should be considered in the community detection process. We propose a framework to extract social interactions and interaction emotions from user-generated contents and a GN-H co-training algorithm to utilize the two types of information in community detection. We show the benefit of including emotion information in community detection using simulated data. We also conduct a case study on a real-world Web forum dataset to exemplify the utility of the framework in identifying communities to support further analysis