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In this paper, we investigate the practical applicability of Co-Training for the task of building a classifier for reference resolution. We are concerned with the question if Co-Training can significantly reduce the amount of manual labeling work and still produce a classifier with an acceptable performance.
We describe a simple and efficient Java object model and application programming interface (API) for (possibly multi-modal) annotated natural language corpora. Corpora are represented as elements like Sentences, Turns, Utterances, Words, Gestures and Markables. The API allows linguists to access corpora in terms of these discourse-level elements, i.e. at a conceptual level they are familiar with, with the flexibility offered by a general purpose programming language. It is also a contribution to corpus standardization efforts because it is based on a straightforward and easily extensible data model which can serve as a target for conversion of different corpus formats.
We present a light-weight tool for the annotation of linguistic data on multiple levels. It is based on the simplification of annotations to sets of markables having attributes and standing in certain relations to each other. We describe the main features of the tool, emphasizing its simplicity, customizability and versatility
We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NP- and non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.
We present an implemented XML data model and a new, simplified query language for multi-level annotated corpora. The new query language involves automatic conversion of queries into the underlying, more complicated MMAXQL query language. It supports queries for sequential and hierarchical, but also associative (e.g. coreferential) relations. The simplified query language has been designed with non-expert users in mind.
We present an implemented machine learning system for the automatic detection of nonreferential it in spoken dialog. The system builds on shallow features extracted from dialog transcripts. Our experiments indicate a level of performance that makes the system usable as a preprocessing filter for a coreference resolution system. We also report results of an annotation study dealing with the classification of it by naive subjects.
We present an implemented system for the resolution of it, this, and that in transcribed multi-party dialog. The system handles NP-anaphoric as well as discourse-deictic anaphors, i.e. pronouns with VP antecedents. Selectional preferences for NP or VP antecedents are determined on the basis of corpus counts. Our results show that the system performs significantly better than a recency-based baseline.
Current Natural Language Processing (NLP) systems feature high-complexity processing pipelines that require the use of components at different levels of linguistic and application specific processing. These components often have to interface with external e.g. machine learning and information retrieval libraries as well as tools for human annotation and visualization. At the UKP Lab, we are working on the Darmstadt Knowledge Processing Software Repository (DKPro) (Gurevych et al., 2007a; Müller et al., 2008) to create a highly flexible, scalable and easy-to-use toolkit that allows rapid creation of complex NLP pipelines for semantic information processing on demand. The DKPro repository consists of several main parts created to serve the purposes of different NLP application areas
The thesis describes a fully automatic system for the resolution of the pronouns 'it', 'this', and 'that' in English unrestricted multi-party dialog. Referential relations considered include both normal NP-antecedence as well as discourse-deictic pronouns. The thesis contains a theoretical part with a comprehensive empiricial study, and a practical part describing machine learning experiments.