Many concept-to-text generation systems require domain-specific linguistic resources to produce high quality texts, but manually constructing these resources can be tedious and costly. Focusing on NaturalOWL, a publicly available state of the art natural language generator for OWL ontologies, we propose methods to extract from the Web sentence plans and natural language names, two of the most important types of domain-specific linguistic resources used by the generator. Experiments show that texts generated using linguistic resources extracted by our methods in a semi-automatic manner, with minimal human involvement, are perceived as being almost as good as texts generated using manually authored linguistic resources, and much better than texts produced by using linguistic resources extracted from the relation and entity identifiers of the ontology.
Simple ontology verbalizers @cite_13 @cite_24 @cite_3 @cite_31 @cite_9 @cite_6 @cite_19 @cite_26 @cite_28 typically produce texts describing individuals and classes without requiring manually authored domain-dependent linguistic resources. They usually tokenize the identifiers or labels (e.g., rdfs:label ) of the individuals or classes to obtain names and sentence plans. Androutsopoulos alt Androutsopoulos2013 showed that the texts of the verbalizer @cite_32 @cite_38 , one of the best publicly available verbalizers, are perceived as being of significantly lower quality compared to texts generated by with domain-dependent linguistic resources; names, sentence plans, and (to a lesser extent) text plans were found to contribute most to this difference. Without domain-dependent linguistic resources, was found to generate texts of the same quality as the verbalizer.
@cite_38 @cite_26 @cite_28 @cite_9 @cite_32 @cite_3 @cite_6 @cite_24 @cite_19 @cite_31 @cite_13
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It has frequently been observed that domain experts are not necessarily ontology experts, and that the production of ontologies would be aided if they could read and edit axioms in natural language. The SWAT Tools Verbaliser is available, via a web interface, for verbalising OWL ontologies as texts in a controlled fragment of English. Taking as input any OWL ontology, the verbaliser creates a lexicon containing entries for all the entities in the input, and uses it to generate an English sentence corresponding to each logical axiom. These sentences are then organised into a document structure similar to that of an encyclopaedia, with an entry providing a definition, typology and examples for each entity. The output is either organised, easily-navigable English text encoded in XML, or a copy of the input OWL in which each entity is annotated with its description entry. The generated texts have been evaluated in a number of ways which are briefly presented here. This paper describes a system and set of algorithms for automatically inducing stand-alone monolingual part-of-speech taggers, base noun-phrase bracketers, named-entity taggers and morphological analyzers for an arbitrary foreign language. Case studies include French, Chinese, Czech and Spanish.Existing text analysis tools for English are applied to bilingual text corpora and their output projected onto the second language via statistically derived word alignments. Simple direct annotation projection is quite noisy, however, even with optimal alignments. Thus this paper presents noise-robust tagger, bracketer and lemmatizer training procedures capable of accurate system bootstrapping from noisy and incomplete initial projections.Performance of the induced stand-alone part-of-speech tagger applied to French achieves 96 core part-of-speech (POS) tag accuracy, and the corresponding induced noun-phrase bracketer exceeds 91 F-measure. The induced morphological analyzer achieves over 99 lemmatization accuracy on the complete French verbal system.This achievement is particularly noteworthy in that it required absolutely no hand-annotated training data in the given language, and virtually no language-specific knowledge or resources beyond raw text. Performance also significantly exceeds that obtained by direct annotation projection. Most previous work on trainable language generation has focused on two paradigms: (a) using a generation decisions of an existing generator. Both approaches rely on the existence of a handcrafted generation component, which is likely to limit their scalability to new domains. The first contribution of this article is to present Bagel, a fully data-driven generation method that treats the language generation task as a search for the most likely sequence of semantic concepts and realization phrases, according to Factored Language Models (FLMs). As domain utterances are not readily available for most natural language generation tasks, a large creative effort is required to produce the data necessary to represent human linguistic variation for nontrivial domains. This article is based on the assumption that learning to produce paraphrases can be facilitated by collecting data from a large sample of untrained annotators using crowdsourcing—rather than a few domain experts—by relying on a coarse meaning representation. A second contribution of this article is to use crowdsourced data to show how dialogue naturalness can be improved by learning to vary the output utterances generated for a given semantic input. Two data-driven methods for generating paraphrases in dialogue are presented: (a) by sampling from the n-best list of realizations produced by Bagel's FLM reranker; and (b) by learning a structured perceptron predicting whether candidate realizations are valid paraphrases. We train Bagel on a set of 1,956 utterances produced by 137 annotators, which covers 10 types of dialogue acts and 128 semantic concepts in a tourist information system for Cambridge. An automated evaluation shows that Bagel outperforms utterance class LM baselines on this domain. A human evaluation of 600 resynthesized dialogue extracts shows that Bagel's FLM output produces utterances comparable to a handcrafted baseline, whereas the perceptron classifier performs worse. Interestingly, human judges find the system sampling from the n-best list to be more natural than a system always returning the first-best utterance. The judges are also more willing to interact with the n-best system in the future. These results suggest that capturing the large variation found in human language using data-driven methods is beneficial for dialogue interaction. Natural Language Generation (NLG) 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 (FB-LTAG) from a parallel corpus of text and data. The resulting grammar includes a unification based semantics and can be used by an existing surface realiser to generate sentences from test 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 uses a supplied lexicon but 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 (classes and relations) and their verbalisation patterns (frames). Based on the observations made, I build different probabilistic models which are used for selection of appropriate frames and syntax semantics linking while verbalising KB inputs. I evaluate the output sentences and analyse the issues relevant to learning from non-parallel corpora. In both these approaches, I use the data derived from an existing biomedical ontology as a reference input. The proposed methods are generic and can be easily adapted for input from other ontologies for which a parallel non-parallel corpora exists.