XML-based data preparation for robust deep parsing

Grover, Claire; Lascarides, Ālex

doi:10.3115/1073012.1073046

Cited by 10 publications

(11 citation statements)

References 9 publications

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“…The analyser was used to create the frequency lists for the sampling frame for the SENSEVAL-1 exercise (Kilgarriff 1998). It forms part of a prototype word sense disambiguation system (Carroll and McCarthy 2000), and is being used in research into methods for the semantic interpretation of complex nominals in medical domains (Grover and Lascarides 2001). Other, commercial applications are in corpus processing for dictionary publishing, in particular as part of the creation of 'word sketches' for lexicographers (Kilgarriff and Rundell 1999), and in product marketing, producing lemma frequency data from large corpora to inform the selection of new brand names.…”

Section: The Morphological Analyser and Generator In Applied Systemsmentioning

confidence: 99%

Applied morphological processing of English

2001

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We describe two newly developed computational tools for morphological processing: a program for analysis of English inflectional morphology, and a morphological generator, automatically derived from the analyser. The tools are fast, being based on finite-state techniques, have wide coverage, incorporating data from various corpora and machine readable dictionaries, and are robust, in that they are able to deal effectively with unknown words. The tools are freely available. We evaluate the accuracy and speed of both tools and discuss a number of practical applications in which they have been put to use.

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Section: The Morphological Analyser and Generator In Applied Systemsmentioning

confidence: 99%

Applied morphological processing of English

2001

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“…To best of our knowledge, this is the first implemented system which integrates high-performance shallow processing with an advanced deep HPSG-based analysis system. There exists only very little other work that considers integration of shallow and deep NLP using an XML-based architecture, most notably (Grover and Lascarides, 2001). However, their integration efforts are largly limited to the level of POS tag information.…”

Section: Discussionmentioning

confidence: 99%

An integrated architecture for shallow and deep processing

Frank

Kiefer

Mueller

et al. 2001

Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02

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We present a flexible 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 real-world German text benefit from a deep grammatical analysis. An Integrated Architecture for Shallow and Deep ProcessingPaper ID: P0270 AbstractWe present a flexible 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 real-world German text benefit from a deep grammatical analysis.

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“…LXGram uses a similar approach, where morphological information output by shallow tools is used to enable the grammar to process un- [ 115 ] known words (though we do not use a shallow parser to improve efficiency). Grover and Lascarides (2001) is an earlier work that also uses the morphological information coming from shallow tools to increase the robustness of a computational grammar, namely when it comes to dealing with out-of-vocabulary words.…”

Section: Hybrid Natural Language Processingmentioning

confidence: 99%

Full-fledged temporal processing: bridging the gap between deep linguistic processing and temporal extraction

Costa

Branco

2013

JLM

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Keywords : temporal processing, temporal extraction, tense, aspect, hybrid approaches, deep linguistic processing, shallow linguistic processing The full-fledged processing of temporal information presents specific challenges. These difficulties largely stem from the fact that the temporal meaning conveyed by grammatical means interacts with many extra-linguistic factors (world knowledge, causality, calendar systems, reasoning). This article proposes a novel approach to this problem, based on a hybrid strategy that explores the complementarity of the symbolic and probabilistic methods. A specialized temporal extraction system is combined with a deep linguistic processing grammar. The temporal extraction system extracts eventualities, times and dates mentioned in text, and also temporal relations between them, in line with the tasks of the recent TempEval challenges; and uses machine learning techniques to draw from different sources of information (grammatical and extra-grammatical) even if it is not explicitly known how these combine to produce the final temporal meaning being expressed. In turn, the deep computational grammar delivers richer truth-conditional meaning representations of input sentences, which include a principled representation of temporal information, on which higher level tasks, including reasoning, can be based. These deep semantic representations are extended and improved according to the output of the aforementioned temporal extraction module. The prototype implemented shows performance results that increase the quality of the temporal meaning representations and are better than the performance of each of the two components in isolation.

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XML-based data preparation for robust deep parsing

Cited by 10 publications

References 9 publications

Applied morphological processing of English

Applied morphological processing of English

An integrated architecture for shallow and deep processing

Full-fledged temporal processing: bridging the gap between deep linguistic processing and temporal extraction

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