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Theses

Surface Realisation from Knowledge Bases

Abstract : Natural Language Generation is the task of automatically producing natural language text to describe information present in non-linguistic data. It involves three main subtasks: (i) selecting the relevant portion of input data; (ii) determining the words that will be used to verbalise the selected data; and (iii) mapping these words into natural language text. The latter task is known as Surface Realisation (SR). In my thesis, I study the SR task in the context of input data coming from Knowledge Bases (KB). I present two novel approaches to surface realisation from knowledge bases: a supervised approach and a weakly supervised approach. In the first, supervised, approach, I present a corpus-based method for inducing a Feature Based Lexicalized Tree Adjoining Grammar from a parallel corpus of text and data. I show that the induced grammar is compact and generalises well over the test data yielding results that are close to those produced by a handcrafted symbolic approach and which outperform an alternative statistical approach. In the weakly supervised approach, I explore a method for surface realisation from KB data which does not require a parallel corpus. Instead, I build a corpus from heterogeneous sources of domain-related text and use it to identify possible lexicalisations of KB symbols and their verbalisation patterns. I evaluate the output sentences and analyse the issues relevant to learning from non-parallel corpora. In both these approaches, the proposed methods are generic and can be easily adapted for input from other ontologies for which a parallel/non-parallel corpora exists
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  • HAL Id : tel-01754499, version 1

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Bikash Gyawali. Surface Realisation from Knowledge Bases. Other [cs.OH]. Université de Lorraine, 2016. English. ⟨NNT : 2016LORR0004⟩. ⟨tel-01754499v1⟩

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