This paper presents a novel approach to ontology localization with the objective of obtaining multilingual ontologies. Within the ontology development process, ontology localization has been defined as the activity of adapting an ontology to a concrete linguistic and cultural community. Depending on the ontology layers – terminological and/or conceptual – involved in the ontology localization activity, three heterogeneous multilingual ontology metamodels have been identified, of which we propose one of them. Our proposal consists in associating the ontology metamodel to an external model for representing and structuring lexical and terminological data in different natural languages. Our model has been called Linguistic Information Repository (LIR). The main advantages of this modelling modality rely on its flexibility by allowing (1) the enrichment of any ontology element with as much linguistic information as needed by the final application, and (2) the establishment of links among linguistic elements within and across different natural languages. The LIR model has been designed as an ontology of linguistic elements and is currently available in Web Ontology Language (OWL). The set of lexical and terminological data that it provides to ontology elements enables the localization of any ontology to a certain linguistic and cultural universe. The LIR has been evaluated against the multilingual requirements of the Food and Agriculture Organization of the United Nations in the framework of the NeOn project. It has proven to solve multilingual representation problems related to the establishment of well-defined relations among lexicalizations within and across languages, as well as conceptualization mismatches among different languages. Finally, we present an extension to the Ontology Metadata Vocabulary, the so-called LexOMV, with the aim of reporting on multilinguality at the ontology metadata level. By adding this contribution to the LIR model, we account for multilinguality at the three levels of an ontology: data level, knowledge representation level and metadata level.

The generation of referring expressions is a central topic in computational linguistics. Natural referring expressions – both definite references like ‘the baseball cap’ and pronouns like ‘it’ – are dependent on discourse context. We examine the practical implications of context-dependent referring expression generation for the design of spoken systems. Currently, not all spoken systems have the goal of generating natural referring expressions. Many researchers believe that the context-dependency of natural referring expressions actually makes systems less usable. Using the dual-task paradigm, we demonstrate that generating natural referring expressions that are dependent on discourse context reduces cognitive load. Somewhat surprisingly, we also demonstrate that practice does not improve cognitive load in systems that generate consistent (context-independent) referring expressions. We discuss practical implications for spoken systems as well as other areas of referring expression generation.

Many parsing techniques assume the use of a packed parse forest to enable efficient and accurate parsing. However, they suffer from an inherent problem that derives from the restriction of locality in the packed parse forest. Deterministic parsing is one solution that can achieve simple and fast parsing without the mechanisms of the packed parse forest by accurately choosing search paths. We propose new deterministic shift-reduce parsing and its variants for unification-based grammars. Deterministic parsing cannot simply be applied to unification-based grammar parsing, which often fails because of its hard constraints. Therefore, this is developed by using default unification, which almost always succeeds in unification by overwriting inconsistent constraints in grammars.

In this work we study how features typically used in natural language processing tasks, together with measures from syntactic complexity, can be adapted to the problem of developing language profiles of bilingual children. Our experiments show that these features can provide high discriminative value for predicting language dominance from story retells in a Spanish–English bilingual population of children. Moreover, some of our proposed features are even more powerful than measures commonly used by clinical researchers and practitioners for analyzing spontaneous language samples of children. This study shows that the field of natural language processing has the potential to make significant contributions to communication disorders and related areas.

In this work we investigate four subjectivity and polarity tasks on spoken and written conversations. We implement and compare several pattern-based subjectivity detection approaches, including a novel technique wherein subjective patterns are learned from both labeled and unlabeled data, using n-gram word sequences with varying levels of lexical instantiation. We compare the use of these learned patterns with an alternative approach of using a very large set of raw pattern features. We also investigate how these pattern-based approaches can be supplemented and improved with features relating to conversation structure. Experimenting with meeting speech and email threads, we find that our novel systems incorporating varying instantiation patterns and conversation features outperform state-of-the-art systems despite having no recourse to domain-specific features such as prosodic cues and email headers. In some cases, such as when working with noisy speech recognizer output, a small set of well-motivated conversation features performs as well as a very large set of raw patterns.