Fuzzy Set Theory-Based Belief Processing for Natural Language Texts

Introduction

The growing number of publicly available information sources makes it impossible for individuals to keep track of all the various opinions on one topic. The goal of our artificial believer system we present in this paper is to extract and analyze opinionated statements from newspaper articles.

Beliefs are modeled with a fuzzy-theoretic approach applied after NLP-based information extraction. A fuzzy believer models a human agent, deciding what statements to believe or reject based on different, configurable strategies.

Workshop on Traceability at CASCON 2007

Together with Juergen Rilling from Concordia University and Philippe Charland from the DRDC Canada I'm organizing a workshop at CASCON 2007: Traceability in Software Engineering—Past, Present and Future. It's on October 25 at the Sheraton Parkway Toronto North Hotel and Convention Centre, Ontario, Canada.

Durm German Lemmatizer v1.0 Released

I'm happy to announce the first public release of our free/open source Durm Lemmatization System for the German language.

The release comes with source code, binaries, documentation, resources (German lexicon, Case Tagger probabilities), and manually annotated texts from the German Wikipedia for evaluation.

Multi-lingual Noun Phrase Chunker Updated

I just posted a small update to my multi-lingual noun phrase chunker (MuNPEx) for GATE.

Changes in v0.2 are:
o preliminary Spanish support (see below)
o renamed from "NPE" to "MuNPEx" in a blatant attempt on Googlewhacking
o small cleanups
o now comes with a sample NE transducer for number markup to improve chunking
Supported languages are now English, German, French, and Spanish (beta).

Attributions

Abstract

We present here the outline of an ongoing research effort to recognize, represent, and interpret attributive constructions such as reported speech in newspaper articles. The role of reported speech is attribution: the statement does not assert some information as `true' but attributes it to some source. The description of the source and the choice of the reporting verb can express the reporter's level of confidence in the attributed material.

Technical Report on Text Mining (in German)

A new technical report on Text Mining (in German) is now available. This is a collection of reports written by students within a Hauptseminar, which was given by yours truly and Jutta Mülle at Universität Karlsruhe, Germany.

Mutation Miner - Textual Annotation of Protein Structures

Abstract

Protein structure visualization tools render images that allow the user to explore structural features of a protein. Context specific information relating to a particular protein or protein family is not easily integrated and must be uploaded from databases or provided through manual curation of input files. We describe a mixed natural language processing and protein sequence analysis approach for the retrieval of mutation specific annotations from full text articles for rendering with protein structures.

Mutation Miner (CPI 2005)

Introduction

Biological researchers today have access to vast amounts of exponentially growing research data in a structured form within several publicly accessible databases. A large proportion of salient information is however still hidden within individual research papers, since costly manual database curation efforts are overwhelmed by the scale of new information being generated. In the domain of protein engineering, critical units of information required from the literature include: the identity of the mutated protein, the identity and position of wild type residues that are mutated, the identity of the resulting mutant residues and the impacts of the mutations on functional properties of the proteins.
Mutation Miner is a system designed to automate the extraction of mutations and textual annotations describing the impacts of mutations on protein properties (mutation annotations) from full text scientific literature. Furthermore, the system retrieves and carries out bioinformatic analyses on mutated sequences providing the mapped coordinates of mutants on a selected structure. Integration of multiple formatted mutation annotations with associated residue coordinates facilitates their rendering with structure visualization tools. We describe the architecture and tools that support Mutation Miner (Text mining-NLP, Sequence Analysis, Structure Visualization) and present performance evaluations that demonstrate the feasibility of this approach.

Mutation Miner (ISMB 2005)

Introduction

Biological researchers today have access to vast amounts of exponentially growing research data in a structured form within several publicly accessible databases. A large proportion of salient information is however still hidden within individual research papers, since costly manual database curation efforts are overwhelmed by the scale of new information being generated. In the domain of protein engineering, critical units of information required from the literature include: the identity of the mutated protein, the identity and position of wild type residues that are mutated, the identity of the resulting mutant residues and the impacts of the mutations on functional properties of the proteins.
Mutation Miner is a system designed to automate the extraction of mutations and textual annotations describing the impacts of mutations on protein properties (mutation annotations) from full text scientific literature. Furthermore, the system retrieves and carries out bioinformatic analyses on mutated sequences providing the mapped coordinates of mutants on a selected structure. Integration of multiple formatted mutation annotations with associated residue coordinates facilitates their rendering with structure visualization tools. We describe the architecture and tools that support Mutation Miner (Text mining-NLP, Sequence Analysis, Structure Visualization) and present performance evaluations that demonstrate the feasibility of this approach.

A Context-Driven Software Comprehension Process Model

Abstract

Comprehension is an essential part of software evolution. Only software that is well understood can evolve in a controlled manner. In this paper, we present a formal process model to support the comprehension of software systems by using Ontology and Description Logic. This formal representation supports the use of reasoning services across different knowl- edge resources and therefore, enables us to provide users with guidance during the comprehension process that is context sensitive to their particular comprehension task. As part of the process model, we also adopt a new interactive story metaphor, to represent the interactions between users and the comprehension process.

Keywords: Software evolution, program comprehension, process modeling, story metaphor, ontological reasoning

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