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In this contribution we present some work of the R&D European project “LIRICS” and of the ISO/TC 37/SC 4 committee related to the topic of interoperability and re-use of language resources. We introduce some basic mechanisms of the standardization work in ISO and describe in more details the general approach on how to cope with the annotation of language data within ISO.
In order to determine priorities for the improvement of timing in synthetic speech this study looks at the role of segmental duration prediction and the role of phonological symbolic representation in the perceptual quality of a text-to-speech system. In perception experiments using German speech synthesis, two standard duration models (Klatt rules and CART) were tested. The input to these models consisted of a symbolic representation which was either derived from a database or a text-to-speech system. Results of the perception experiments show that different duration models can only be distinguished when the symbolic representation is appropriate. Considering the relative importance of the symbolic representation, post-lexical segmental rules were investigated with the outcome that listeners differ in their preferences regarding the degree of segmental reduction. As a conclusion, before fine-tuning the duration prediction, it is important to derive an appropriate phonological symbolic representation in order to improve timing in synthetic speech.
In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge by training a supervised classifier with in-domain features, such as bag of words, on instances labeled by a rule-based classifier. Thus, this approach can be considered as a simple and effective method for domain adaptation. Among the list of components of this approach, we investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. In particular, the former addresses the issue in how far linguistic modeling is relevant for this task. We not only examine how this method performs under more difficult settings in which classes are not balanced and mixed reviews are included in the data set but also compare how this linguistically-driven method relates to state-of-the-art statistical domain adaptation.
In this article, we explore the feasibility of extracting suitable and unsuitable food items for particular health conditions from natural language text. We refer to this task as conditional healthiness classification. For that purpose, we annotate a corpus extracted from forum entries of a food-related website. We identify different relation types that hold between food items and health conditions going beyond a binary distinction of suitability and unsuitability and devise various supervised classifiers using different types of features. We examine the impact of different task-specific resources, such as a healthiness lexicon that lists the healthiness status of a food item and a sentiment lexicon. Moreover, we also consider task-specific linguistic features that disambiguate a context in which mentions of a food item and a health condition co-occur and compare them with standard features using bag of words, part-of-speech information and syntactic parses. We also investigate in how far individual food items and health conditions correlate with specific relation types and try to harness this information for classification.
We continue the study of the reproducibility of Propp’s annotations from Bod et al. (2012). We present four experiments in which test subjects were taught Propp’s annotation system; we conclude that Propp’s system needs a significant amount of training, but that with sufficient time investment, it can be reliably trained for simple tales.
The paper investigates the evolution of document grammars from a linguistic point of view. Document grammars have been developed in the past decades in order to formalize knowledge on the structure of textual information. A well-known instance of a document grammar is the »Document Type Definition« (DTD) as part of the Extensible Markup Language (XML). DTDs allow to define so-called tree grammars that constrain the application of tag-sets in the process of annotation of a document. In an XML-based document workflow, DTDs play a crucial role for validation and transforming huge amounts of texts in standardized data formats. An interesting point in the development of XML DTDs is the fact that the restriction of the formal expressiveness paved the way to understand the formal properties of document grammars better and to develop more a powerful version like XML Schema recently. In this sense, the simplicity of the original approach, resulting from the necessary restriction of previous approaches, yielded new complexity on formally understood grounds.
This article introduces the topic of ‘‘Multilingual language resources and interoperability’’. We start with a taxonomy and parameters for classifying language resources. Later we provide examples and issues of interoperatability, and resource architectures to solve such issues. Finally we discuss aspects of linguistic formalisms and interoperability.