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AXEL: A framework to deal with ambiguity in three-noun compounds
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 6/12/2010.Cognitive Linguistics has been widely used to deal with the ambiguity generated by words in combination. Although this domain offers many solutions to address this challenge, not all of them can be implemented in a computational environment. The Dynamic Construal of Meaning framework is argued to have this ability because it describes an intrinsic degree of association of meanings, which in turn, can be translated into computational programs. A limitation towards a computational approach, however, has been the lack of syntactic parameters. This research argues that this limitation could be overcome with the aid of the Generative Lexicon Theory (GLT). Specifically, this dissertation formulated possible means to marry the GLT and Cognitive Linguistics in a novel rapprochement between the two.
This bond between opposing theories provided the means to design a computational template (the AXEL System) by realising syntax and semantics at software levels. An instance of the AXEL system was created using a Design Research approach. Planned iterations were involved in the development to improve artefact performance. Such iterations boosted performance-improving, which accounted for the degree of association of meanings in three-noun compounds.
This dissertation delivered three major contributions on the brink of a so-called turning point in Computational Linguistics (CL). First, the AXEL system was used to disclose hidden lexical patterns on ambiguity. These patterns are difficult, if not impossible, to be identified without automatic techniques. This research claimed that these patterns can assist audiences of linguists to review lexical knowledge on a software-based viewpoint.
Following linguistic awareness, the second result advocated for the adoption of improved resources by decreasing electronic space of Sense Enumerative Lexicons (SELs). The AXEL system deployed the generation of “at the moment of use” interpretations, optimising the way the space is needed for lexical storage.
Finally, this research introduced a subsystem of metrics to characterise an ambiguous degree of association of three-noun compounds enabling ranking methods. Weighing methods delivered mechanisms of classification of meanings towards Word Sense Disambiguation (WSD). Overall these results attempted to tackle difficulties in understanding studies of Lexical Semantics via software tools
Generation of multilingual ontology lexica with M-ATOLL : a corpus-based approach for the induction of ontology lexica
Walter S. Generation of multilingual ontology lexica with M-ATOLL : a corpus-based approach for the induction of ontology lexica. Bielefeld: Universität Bielefeld; 2017.There is an increasing interest in providing common web users with access to structured knowledge bases such as DBpedia, for example by means of question answering systems.
All such question answering systems have in common that they have to map a natural language input, be it spoken or written, to a formal representation in order to extract the correct answer from the target knowledge base.
This is also the case for systems which generate natural language text from a given knowledge base.
The main challenge is how to map natural language (spoken or written) to structured data and vice versa.
To this end, question answering systems require knowledge about how the vocabulary elements used in the available datasets are verbalized in natural language, covering different verbalization variants.
Multilinguality of course increases the complexity of this challenge.
In this thesis we introduce M-ATOLL, a framework for automatically inducing ontology lexica in multiple languages, to find such verbalization variants.
We have instantiated the system for three languages, English, German and Spanish, by exploiting a set of language-specific dependency patterns for finding lexicalizations in text corpora. Additionally, we extended our framework to extract complex adjective lexicalizations with a machine-learning-based approach.
M-ATOLL is the first open-source and multilingual approach for the generation of ontology lexica. In this thesis we present grammatical patterns for three different languages, on which the extraction of lexicalization relies.
We provide an analysis of these patterns as well as a comparison with those proposed by other state-of-the-art systems. Additionally, we present a detailed evaluation comparing the different approaches with different settings on a publicly available goldstandard, and discuss their potential and limitations