Assessing deaf and hearing children`s communication in Brazil

In Brazil there are no specific tests for either signed or spoken language for deaf children. A protocol evaluating communicative abilities independent of modality of communication (sign language or spoken language), and comprising assessments of (a) pragmatic profile; (b) modality of communication and linguistic level; (c) complexity of communication; and (d) style and efficacy of communication between parent and child was administered to 127 deaf and hearing children. The children, aged 3-6 years old, were distributed in three groups: 20 with severe hearing loss, 40 with profound hearing loss and 67 normally hearing. Deaf children were found to be delayed, independent of their linguistic level and preferred modality of communication. The protocol in this study proved to be an useful instrument for gathering relevant information about the three groups of preschool children`s communicative abilities, and particularly suitable for use in countries where standardized assessments are not available. Learning outcomes: The reader will be introduced to the use of an assessment protocol comprising its development, application and data analysis. The reader will be informed about assessment of deaf children`s preferred modality of communication, by the participation of a bilingual (sign language user) professional. Communication abilities can be assessed independently of the linguistic modality. In developing countries in general, where simple and easy to administer assessments tools are scarce, such a protocol is of specific value. (c) 2010 Elsevier Inc. All rights reserved.

Cross-cultural adaptation and validation of a Quality of Life questionnaire: The Nasal Obstruction Symptom Evaluation questionnaire

Background:The Nasal Obstruction Symptom Evaluation (NOSE) instrument is a disease-specific questionnaire for assessing the outcome of an intervention in nasal obstruction in trials. This instrument is only available in the English language and cross-culturally valid questionnaires are very important for all research, including nasal obstruction. The aim of the current study was to reproduce the cross-cultural adaptation process for the NOSE questionnaire in the Portuguese language (NOSE-p). Methodology: Cross-cultural adaptation and validation of the instrument were divided into two stages. Stage I involved four bilingual professionals, an expert committee and the author of the original instrument. In Stage 2, the NOSE-p was tested on 33 patients undergoing septoplasty for internal consistency, test-retest reliability, construct validity. discriminant validity, criterion validity, and response sensitivity. Results: The cross-cultural adaptation process was completed and the NOSE-p was demonstrated to be a valid instrument with satisfactory construct validity. It showed an adequate internal consistency reliability and adequate test-retest reliability. It could discriminate between patients with and without nasal obstruction and it has a high response sensitivity to change. Conclusions: The cross-cultural adaptation and validation process demonstrated to be valid and the NOSE-p proved to be applicable in Brazil.

An investigation into feature construction to assist word sense disambiguation

Identifying the correct sense of a word in context is crucial for many tasks in natural language processing (machine translation is an example). State-of-the art methods for Word Sense Disambiguation (WSD) build models using hand-crafted features that usually capturing shallow linguistic information. Complex background knowledge, such as semantic relationships, are typically either not used, or used in specialised manner, due to the limitations of the feature-based modelling techniques used. On the other hand, empirical results from the use of Inductive Logic Programming (ILP) systems have repeatedly shown that they can use diverse sources of background knowledge when constructing models. In this paper, we investigate whether this ability of ILP systems could be used to improve the predictive accuracy of models for WSD. Specifically, we examine the use of a general-purpose ILP system as a method to construct a set of features using semantic, syntactic and lexical information. This feature-set is then used by a common modelling technique in the field (a support vector machine) to construct a classifier for predicting the sense of a word. In our investigation we examine one-shot and incremental approaches to feature-set construction applied to monolingual and bilingual WSD tasks. The monolingual tasks use 32 verbs and 85 verbs and nouns (in English) from the SENSEVAL-3 and SemEval-2007 benchmarks; while the bilingual WSD task consists of 7 highly ambiguous verbs in translating from English to Portuguese. The results are encouraging: the ILP-assisted models show substantial improvements over those that simply use shallow features. In addition, incremental feature-set construction appears to identify smaller and better sets of features. Taken together, the results suggest that the use of ILP with diverse sources of background knowledge provide a way for making substantial progress in the field of WSD.