Objective Assessment of Internal Nasal Dimensions and Speech Resonance in Individuals With Repaired Unilateral Cleft Lip and Palate After Rhinoseptoplasty

The objective of the current study was to analyze the effects of rhinoseptoplasty on internal nasal dimensions and speech resonance of individuals with unilateral cleft lip and palate, estimated by acoustic rhinometry and nasometry, respectively. Twenty-one individuals (aged 15-46 years) with previously repaired unilateral cleft lip and palate were analyzed before (PRE), and 6 to 9 (POST1) and 12 to 18 months (POST2) after surgery. Acoustic rhinometry was used to measure the cross-sectional areas (CSAs) of segments corresponding to the nasal valve (CSA1), anterior portion (CSA2), and posterior portion (CSA3) of the lower turbinate, and the volumes at the nasal valve (V1) and turbinate (V2) regions at cleft and noncleft sides, before and after nasal decongestion with a topical vasoconstrictor. Nasometry was used to evaluate speech nasalance during the reading of a set of sentences containing nasal sounds and other devoid of nasal sounds. At the cleft side, before nasal decongestion, there was a significant increase (P < 0.05) in mean CSA1 and V1 values at POST1 and POST2 compared with PRE. After decongestion, increased values were also observed for CSA2 and V2 at POST2. No significant changes were observed at the noncleft side. Mean nasalance values at PRE, POST1, an POST2 were not different from each other in both oral and nasal sentences. The measurement of CSAs and volumes by acoustic rhinometry revealed that rhinoseptoplasty provided, in most cases analyzed, a significant increase in nasal patency, without concomitant changes in speech resonance, as estimated by nasalance assessment.

OntoMap: an ontology-based architecture to perform the semantic mapping between an interlingua and software components

This paper is about the use of natural language to communicate with computers. Most researches that have pursued this goal consider only requests expressed in English. A way to facilitate the use of several languages in natural language systems is by using an interlingua. An interlingua is an intermediary representation for natural language information that can be processed by machines. We propose to convert natural language requests into an interlingua [universal networking language (UNL)] and to execute these requests using software components. In order to achieve this goal, we propose OntoMap, an ontology-based architecture to perform the semantic mapping between UNL sentences and software components. OntoMap also performs component search and retrieval based on semantic information formalized in ontologies and rules.

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. (c) 2008 Elsevier Inc. All rights reserved.