Anette Frank

Question answering from structured knowledge sources

Abstract 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 .

Ontology-based information extraction and integration from heterogeneous data sources

In this paper we present the design, implementation and evaluation of SOBA, a system for ontology-based information extraction from heterogeneous data resources, including plain text, tables and image captions. SOBA is capable of processing structured information, text and image captions to extract information and integrate it into a coherent knowledge base. To establish coherence, SOBA interlinks the information extracted from different sources and detects duplicate information. The knowledge base produced by SOBA can then be used to query for information contained in the different sources in an integrated and seamless manner. Overall, this allows for advanced retrieval functionality by which questions can be answered precisely. A further distinguishing feature of the SOBA system is that it straightforwardly integrates deep and shallow natural language processing to increase robustness and accuracy. We discuss the implementation and application of the SOBA system within the SmartWeb multimodal dialog system. In addition, we present a thorough evaluation of the different components of the system. However, an end-to-end evaluation of the whole SmartWeb system is out of the scope of this paper and has been presented elsewhere by the SmartWeb consortium.

Integrated Shallow and Deep Parsing: TopP Meets HPSG

We present a novel, data-driven method for integrated shallow and deep parsing. Mediated by an XML-based multi-layer annotation architecture, we interleave a robust, but accurate stochastic topological field parser of German with a constraint-based HPSG parser. Our annotation-based method for dovetailing shallow and deep phrasal constraints is highly flexible, allowing targeted and fine-grained guidance of constraint-based parsing. We conduct systematic experiments that demonstrate substantial performance gains.

An Integrated Archictecture for Shallow and Deep Processing

We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis.

A Semantic Approach To Textual Entailment: System Evaluation and Task Analysis

This paper discusses our contribution to the third RTE Challenge -- the SALSA RTE system. It builds on an earlier system based on a relatively deep linguistic analysis, which we complement with a shallow component based on word overlap. We evaluate their (combined) performance on various data sets. However, earlier observations that the combination of features improves the overall accuracy could be replicated only partly.

Constraint-based RMRS construction from shallow grammars

We present a constraint-based syntax-semantics interface for the construction of RMRS (Robust Minimal Recursion Semantics) representations from shallow grammars. The architecture is designed to allow modular interfaces to existing shallow grammars of various depth - ranging from chunk grammars to context-free stochastic grammars. We define modular semantics construction principles in a typed feature structure formalism that allow flexible adaptation to alternative grammars and different languages.

From Treebank Resources to LFG F-Structures

We present two companion methods for automatically enriching phrase-structure oriented treebank resources with functional structures. Both methods define systematic patterns of correspondence between partial PS configurations and functional structures. These are applied to PS rules extracted from treebanks, or to flat term representations of treebank trees.

Contextual phenomena and thematic relations in database QA dialogues: results from a Wizard-of-Oz Experiment

Considering data obtained from a corpus of database QA dialogues, we address the nature of the discourse structure needed to resolve the several kinds of contextual phenomena found in our corpus. We look at the thematic relations holding between questions and the preceding context and discuss to which extent thematic related-ness plays a role in discourse structure.

A stochastic topological parser for German

We present a new approach to topological parsing of German which is corpus-based and built on a simple model of probabilistic CFG parsing. The topological field model of German provides a linguistically motivated, flat macro structure for complex sentences. Besides the practical aspect of developing a robust and accurate topological parser for hybrid shallow and deep NLP, we investigate to what extent topological structures can be handled by context-free probabilistic models. We discuss experiments with systematic variants of a topological treebank grammar, which yield competitive results.

A Resource-Poor Approach for Linking Ontology Classes to Wikipedia Articles

The applicability of ontologies for natural language processing depends on the ability to link ontological concepts and relations to their realisations in texts. We present a general, resource-poor account to create such a linking automatically by extracting Wikipedia articles corresponding to ontology classes. We evaluate our approach in an experiment with the Music Ontology. We consider linking as a promising starting point for subsequent steps of information extraction.