Background

Natural language processing frameworks like GATE and UIMA have significantly changed the way NLP applications are designed, developed, and deployed. Features such as component-based design, test-driven development, and resource meta-descriptions now routinely provide higher robustness, better reusability, faster deployment, and improved scalability. They have become the staple of both NLP research and industrial application, fostering a new generation of NLP users and developers.

These are the proceedings of the workshop New Challenges for NLP Frameworks (NLPFrameworks 2010), held in conjunction with LREC 2010, which brought together users and developers of major NLP frameworks.

Introduction

Poster at BioNLP 2010: Ontology-Based Extraction and Summarization of Protein Mutation Impact InformationNLP methods for extracting mutation information from the bibliome have become an important new research area within bio-NLP, as manually curated databases, like the Protein Mutant Database (PMD) (Kawabata et al., 1999), cannot keep up with the rapid pace of mutation research. However, while significant progress has been made with respect to mutation detection, the automated extraction of the impacts of these mutations has so far not been targeted. In this paper, we describe the first work to automatically summarize impact information from protein mutations. Our approach is based on populating an OWL-DL ontology with impact information, which can then be queried to provide structured information, including a summary.

Abstract

We present a lightweight, user-centred approach for document navigation and analysis that is based on an ontology of text mining results. This allows us to bring the result of existing text mining pipelines directly to end users. Our approach is domain-independent and relies on existing NLP analysis tasks such as automatic multi-document summarization, clustering, question-answering, and opinion mining. Users can interactively trigger semantic processing services for tasks such as analyzing product reviews, daily news, or other document sets.

Abstract

An important software engineering artefact used by developers and maintainers to assist in software comprehension and maintenance is source code documentation. It provides insights that help software engineers to effectively perform their tasks, and therefore ensuring the quality of the documentation is extremely important. Inline documentation is at the forefront of explaining a programmer's original intentions for a given implementation. Since this documentation is written in natural language, ensuring its quality needs to be performed manually. In this paper, we present an effective and automated approach for assessing the quality of inline documentation using a set of heuristics, targeting both quality of language and consistency between source code and its comments. We apply our tool to the different modules of two open source applications (ArgoUML and Eclipse), and correlate the results returned by the analysis with bug defects reported for the individual modules in order to determine connections between documentation and code quality.

Abstract

Real-time access to complex knowledge is a business driver in the contact centre environment. In this paper we outline for the domain of telecom technical product support a knowledge sharing paradigm in which a desktop client annotates named entities in technical documents with canonical names, class names or relevant class axioms, derived from an ontology by means of a web services framework. We described the system and its core components; OWL-DL telecom hardware ontology, ontological-natural language processing pipeline, an ontology axiom‐extractor; and the Semantic Assistants framework.

Abstract

Source code contains a large amount of natural language text, particularly in the form of comments, which makes it an emerging target of text analysis techniques. Due to the mix with program code, it is difficult to process source code comments directly within NLP frameworks such as GATE. Within this work we present an effective means for generating a corpus using information found in source code and in-line documentation, by developing a custom doclet for the Javadoc tool. The generated corpus uses a schema that is easily processed by NLP applications, which allows language engineers to focus their efforts on text analysis tasks, like automatic quality control of source code comments. The SSLDoclet is available as open source software.

Abstract

Screenshot of MultiPAX resultsIn this paper, we describe the open source GATE component PAX for extracting predicate-argument structures (PASs). PASs are used in various contexts to represent relations within a sentence structure. Different ``semantic'' parsers extract relational information from sentences but there exists no common format to store this information. Our predicate-argument extractor component (PAX) takes the annotations generated by selected parsers and transforms the parsers' results to predicate-argument structures represented as triples (subject-verb-object). This allows downstream components in an analysis pipeline to process PAS triples independent of the deployed parser, as well as combine the results from several parsers within a single pipeline.

Abstract

Ontology population from text is becoming increasingly important for NLP applications. Ontologies in OWL format provide for a standardized means of modeling, querying, and reasoning over large knowledge bases. Populated from natural language texts, they offer significant advantages over traditional export formats, such as plain XML. The development of text analysis systems has been greatly facilitated by modern NLP frameworks, such as the General Architecture for Text Engineering (GATE). However, ontology population is not currently supported by a standard component. We developed a GATE resource called the OwlExporter that allows to easily map existing NLP analysis pipelines to OWL ontologies, thereby allowing language engineers to create ontology population systems without requiring extensive knowledge of ontology APIs. A particular feature of our approach is the concurrent population and linking of a domain- and NLP-ontology, including NLP-specific features such as safe reasoning over coreference chains.

Abstract

Digitizing a historical document using ontologies and natural language processing techniques can transform it from arcane text to a useful knowledge base.