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

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

Genescene: Biomedical Text and Data Mining

To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. GeneScene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching

Augmented trading:From news articles to stock price predictions using semantics

This thesis tries to answer the question how to predict the reaction of the stock market to news articles using the latest suitable developments in Natural Language Processing. This is done using text classiffication where a new article is matched to a category of articles which have a certain influence on the stock price. The thesis first discusses why analysis of news articles is a feasible approach to predicting the stock market and why analysis of past prices should not be build upon. From related work in this domain two main design choices are extracted; what to take as features for news articles and how to couple them with the changes in stock price. This thesis then suggests which different features are possible to extract from articles resulting in a template for features which can deal with negation, favorability, abstracts from companies and uses domain knowledge and synonyms for generalization. To couple the features to changes in stock price a survey is given of several text classiffication techniques from which it is concluded that Support Vector Machines are very suitable for the domain of stock prices and extensive features. The system has been tested with a unique data set of news articles for which results are reported that are signifficantly better than random. The results improve even more when only headlines of news articles are taken into account. Because the system is only tested with closing prices it cannot concluded that it will work in practice but this can be easily tested if stock prices during the days are available. The main suggestions for feature work are to test the system with this data and to improve the filling of the template so it can also be used in other areas of favorability analysis or maybe even to extract interesting information out of texts

Text-mining and information-retrieval services for molecular biology

Text-mining in molecular biology - defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents - has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators

Nominalization and Alternations in Biomedical Language

Background: This paper presents data on alternations in the argument structure of common domain-specific verbs and their associated verbal nominalizations in the PennBioIE corpus. Alternation is the term in theoretical linguistics for variations in the surface syntactic form of verbs, e.g. the different forms of stimulate in FSH stimulates follicular development and follicular development is stimulated by FSH. The data is used to assess the implications of alternations for biomedical text mining systems and to test the fit of the sublanguage model to biomedical texts. Methodology/Principal Findings: We examined 1,872 tokens of the ten most common domain-specific verbs or their zerorelated nouns in the PennBioIE corpus and labelled them for the presence or absence of three alternations. We then annotated the arguments of 746 tokens of the nominalizations related to these verbs and counted alternations related to the presence or absence of arguments and to the syntactic position of non-absent arguments. We found that alternations are quite common both for verbs and for nominalizations. We also found a previously undescribed alternation involving an adjectival present participle. Conclusions/Significance: We found that even in this semantically restricted domain, alternations are quite common, and alternations involving nominalizations are exceptionally diverse. Nonetheless, the sublanguage model applies to biomedica