The DeepThought Core Architecture Framework

The research performed in the DeepThought project aims at demonstrating the potential of deep linguistic processing if combined with shallow methods for robustness. Classical information retrieval is extended by high precision concept indexing and relation detection. On the basis of this approach, the feasibility of three ambitious applications will be demonstrated, namely: precise information extraction for business intelligence; email response management for customer relationship management; creativity support for document production and collective brainstorming. Common to these applications, and the basis for their development is the XML-based, RMRS-enabled core architecture framework that will be described in detail in this paper. The framework is not limited to the applications envisaged in the DeepThought project, but can also be employed e.g. to generate and make use of XML standoff annotation of documents and linguistic corpora, and in general for a wide range of NLP-based applications and research purposes

Hybrid robust deep and shallow semantic processing for creativity support in document production

The research performed in the DeepThought project (http://www.project-deepthought.net) aims at demonstrating the potential of deep linguistic processing if added to existing shallow methods that ensure robustness. Classical information retrieval is extended by high precision concept indexing and relation detection. We use this approach to demonstrate the feasibility of three ambitious applications, one of which is a tool for creativity support in document production and collective brainstorming. This application is described in detail in this paper. Common to all three applications, and the basis for their development is a platform for integrated linguistic processing. This platform is based on a generic software architecture that combines multiple NLP components and on robust minimal recursive semantics (RMRS) as a uniform representation language

Fachrichtung Informatik Universität des Saarlandes Efficient Parsing with Large-Scale Unification Grammars

The efficiency problem in parsing with large-scale unification grammars, including implementations in the Head-driven Phrase Structure grammar (HPSG) framework, used to be a serious obstacle to their application in research and commercial settings. Over the past few years, however, significant progress in efficient processing has been achieved. Still, many of the proposed techniques were developed in isolation only, making comparison and the assessment of their combined potential difficult. Also, a number of techniques were never evaluated on large-scale grammars. This thesis sets out to improve this situation by reviewing, integrating, and evaluating a number of techniques for efficient unification-based parsing. A strong focus is set on efficient graph unification. I provide an overview of previous work in this area of research, including the foundational algorithm in the work of Wroblewski (1987), for which I identify a previously unnoticed flaw, and provide a solution. I introduce the PET platform, which has been developed with two goals: (i) to serve as a flexible basis for research in efficient processing techniques, allowing precise empirical study and comparison of different approaches, an

Question answering from structured knowledge sources

We present an implemented approach for domain-restricted question answering from structured knowledge sources, based on robust semantic analysis in a hybrid NLP system architecture. We perform question interpretation and answer extraction in an architecture that builds on a lexical-conceptual structure for question interpretation, which is interfaced with domain-specific concepts and properties in a structured knowledge base. Question interpretation involves a limited amount of domain-specific inferences, and accounts for higher-level quantificational questions. Question interpretation and answer extraction are modular components that interact in clearly defined ways. We derive so-called proto queries from the linguistic representations, which provide partial constraints for answer extraction from the underlying knowledge sources. The search queries we construct from proto queries effectively compute minimal spanning trees from the underlying knowledge sources. Our approach naturally extends to multilingual question answering, and has been developed as a prototype system for two application domains: the domain of Nobel prize winners, and the domain of Language Technology, on the basis of the large ontology underlying the information portal LT World