A robotic catheter system includes a controller with a master input device. An instrument driver is in communication with the controller and has a guide instrument interface including a plurality of guide instrument drive elements responsive to control signals generated, at least in part, by the master input device. An elongate guide instrument has a base, distal end, and a working lumen, wherein the guide instrument base is operatively coupled to the guide instrument interface. The guide instrument includes a plurality of guide instrument control elements operatively coupled to respective guide drive elements and secured to the distal end of the guide instrument. The guide instrument control elements are axially moveable relative to the guide instrument such that movement of the guide instrument distal end may be controlled by the master input device.

Robotic catheter system

This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative.The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.

Sentiment analysis: capturing favorability using natural language processing

We present an application of kernel methods to extracting relations from unstructured natural language sources. We introduce kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels. We use the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting person-affiliation and organization-location relations from text. We experimentally evaluate the proposed methods and compare them with feature-based learning algorithms, with promising results.

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Kernel methods for relation extraction

/pdf/kernel-methods-for-relation-extraction-8n9ons765o.pdf

Kernel Methods for Relation Extraction

Subjectivity in natural language refers to aspects of language used to express opinions, evaluations, and speculations. There are numerous natural language processing applications for which subjectivity analysis is relevant, including information extraction and text categorization. The goal of this work is learning subjective language from corpora. Clues of subjectivity are generated and tested, including low-frequency words, collocations, and adjectives and verbs identified using distributional similarity. The features are also examined working together in concert. The features, generated from different data sets using different procedures, exhibit consistency in performance in that they all do better and worse on the same data sets. In addition, this article shows that the density of subjectivity clues in the surrounding context strongly affects how likely it is that a word is subjective, and it provides the results of an annotation study assessing the subjectivity of sentences with high-density features. Finally, the clues are used to perform opinion piece recognition (a type of text categorization and genre detection) to demonstrate the utility of the knowledge acquired in this article.

http://ensani.ir/storage/files/20101126155304-344.pdf

Learning Subjective Language

This paper reports on a large-scale, end-to-end relation and event extraction system. At present, the system extracts a total of 100 types of relations and events, which represents a much wider coverage than is typical of extraction systems. The system consists of three specialized pattern-based tagging modules, a high-precision coreference resolution module, and a configurable template generation module. We report quantitative evaluation results, analyze the results in detail, and discuss future directions.

REES: A Large-Scale Relation and Event Extraction System

A system and method is provided for automatically identifying information in unstructured text and extracting data representing certain types of information from the text to produce a structured set of templates with the extracted data. The newly structured data is then delivered to different types of user devices in accordance stored criteria associated with each type of device.

Content distribution system and method

In our planning for MUC-7, we made the decision to concentrate our e orts in two areas: improvements to our infrastructure, both in system architecture and the supporting set of tools; second, maximizing the performance of each information extraction (IE) module, including the introduction of trainable learningbased modules. We felt that achieving our performance goals in extraction depended on improvements in these areas. Speci cally, our goals when entering MUC-7 were to:

SRA: Description of the IE2 System Used for MUC-7

The Enteropathogen Resource Integration Center (ERIC; http://www.ericbrc.org
 ) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as Escherichia coli and Salmonella spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process. We have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc.) and over 70% for relations (gene/gene product to role, etc). This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application. Our Text Mining application is available online on the ERIC website http://www.ericbrc.org/portal/eric/articles
 . The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations systems.

/pdf/text-mining-of-pubmed-abstracts-by-natural-language-4l7hn831a4.pdf

Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens

This paper describes the Natural Language Processing (NLP) component of an e-mail monitoring product called Assentor®. Assentor monitors electronic correspondence for brokerage firms. It uses pattern-matching-based information extraction technology to find and quarantine e-mail messages that indicate, among others, customer complaints, insider trading, stock hyping, hard-pressure sales tactics, and firm preservation issues such as jokes and obscenities. This paper presents a quantitative evaluation of applying pattern matching vs. keyword-based searching to e-mail monitoring. Our evaluation shows that pattern matching performs significantly better than keyword-based searching both in terms of recall (false negatives) and precision (false positives).

/pdf/assentor-an-nlp-based-solution-to-e-mail-monitoring-2f81d6l9wc.pdf

Mila Ramos-Santacruz

Papers

REES: A Large-Scale Relation and Event Extraction System

Content distribution system and method

SRA: Description of the IE2 System Used for MUC-7

Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens

Assentor®: An NLP-Based Solution to E-mail Monitoring