High-resolution ice thickness and bed topography of a land-terminating section of the Greenland Ice Sheet, with links to GeoTIFFs and text files

We present ice thickness and bed topography maps with a high spatial resolution (250-500 m) of a land-terminating section of the Greenland Ice Sheet derived from ground-based and airborne radar surveys. The data have a total area of ~12 000 km^2 and cover the whole ablation area of the outlet glaciers of Isunnguata Sermia, Russell, Leverett, Ørkendalen and Isorlersuup up to the long-term mass balance equilibrium line altitude at ~1600 m above sea level. The bed topography shows highly variable subglacial trough systems, and the trough of Isunnguata Sermia Glacier is overdeepened and reaches an elevation of ~500 m below sea level. The ice surface is smooth and only reflects the bedrock topography in a subtle way, resulting in a highly variable ice thickness. The southern part of our study area consists of higher bed elevations compared to the northern part. The compiled data sets of ground-based and airborne radar surveys cover one of the most studied regions of the Greenland Ice Sheet and can be valuable for detailed studies of ice sheet dynamics and hydrology.

Frank Willaert, Herman Braet, Thom Mertens y Theo Venckeleer, eds., Medieval Memory. Image and Text , Turnhout: Brepols, Fédération Internationale des Instituts d'Études Médiévales, Textes et Études duMoyen Âge, 27, 2004

Fil: Disalvo, Santiago Aníbal. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación; Argentina.

Text Comprehension And roduction in University Students: Text Reformulation

This paper sets out to report on findings about features of task-specific reformulation observed in university students in the middle stretch of the Psychology degree course (N=58) and in a reference group of students from the degree courses in Modern Languages, Spanish and Library Studies (N=33) from the National University of La Plata (Argentina). Three types of reformulation were modeled: summary reformulation, comprehensive and productive reformulation.The study was based on a corpus of 621 reformulations rendered from different kinds of text. The versions obtained were categorised according to the following criteria: presence or absence of normative, morphosyntactic and semantic difficulties. Findings show that problems arise particularly with paraphrase and summary writing. Observation showed difficulties concerning punctuation, text cohesion and coherence , and semantic distortion or omission as regards extracting and/or substituting gist, with limited lexical resources and confusion as to suitability of style/register in writing. The findings in this study match those of earlier, more comprehensive research on the issue and report on problems experienced by a significant number of university students when interacting with both academic texts and others of a general nature. Moreover, they led to questions, on the one hand, as to the nature of such difficulties, which appear to be production-related problems and indirectly account for inadequate text comprehension, and on the other hand, as to the features of university tuition when it comes to text handling.

Frank Willaert, Herman Braet, Thom Mertens y Theo Venckeleer, eds., Medieval Memory. Image and Text , Turnhout: Brepols, Fédération Internationale des Instituts d'Études Médiévales, Textes et Études duMoyen Âge, 27, 2004

Fil: Disalvo, Santiago Aníbal. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación; Argentina.

Text Comprehension And roduction in University Students: Text Reformulation

This paper sets out to report on findings about features of task-specific reformulation observed in university students in the middle stretch of the Psychology degree course (N=58) and in a reference group of students from the degree courses in Modern Languages, Spanish and Library Studies (N=33) from the National University of La Plata (Argentina). Three types of reformulation were modeled: summary reformulation, comprehensive and productive reformulation.The study was based on a corpus of 621 reformulations rendered from different kinds of text. The versions obtained were categorised according to the following criteria: presence or absence of normative, morphosyntactic and semantic difficulties. Findings show that problems arise particularly with paraphrase and summary writing. Observation showed difficulties concerning punctuation, text cohesion and coherence , and semantic distortion or omission as regards extracting and/or substituting gist, with limited lexical resources and confusion as to suitability of style/register in writing. The findings in this study match those of earlier, more comprehensive research on the issue and report on problems experienced by a significant number of university students when interacting with both academic texts and others of a general nature. Moreover, they led to questions, on the one hand, as to the nature of such difficulties, which appear to be production-related problems and indirectly account for inadequate text comprehension, and on the other hand, as to the features of university tuition when it comes to text handling.

Populäres Handbuch der Landwirthschaft in besonderer Beziehung für den würdigen Bauernstand im Großherzogthum Baden, Hessen, Königreich Würtemberg und deren benachbarten Ländern nach dem gegenwärtigen Standpunkte der Fortschritte im Acker-, Wiesen- und Weinbau, in der Obstbaumzucht, der Rindvieh-, Pferde-, Schweine- und Bienenzucht : eine von der im Jahre 1840 in Brünn abgehaltenen Versammlung deutscher Land- und Forstwirthe gekrönte Preisschrift : mit zwischen den Text gedruckten Zeichnungen

von J. A. Schlipf

Developing Accessible Mobile Phone Applications: The Case of a Contact Manager and Real Time Text Applications

Mobile phones are becoming increasingly popular and are already the first access technology to information and communication. However, people with disabilities have to face a lot of barriers when using this kind of technology. This paper presents an Accessible Contact Manager and a Real Time Text application, designed to be used by all users with disabilities. Both applications are focused to improve accessibility of mobile phones.

Hybrid Approach Combining Machine Learning and a Rule-Based Expert System for Text Categorization

This paper discusses a novel hybrid approach for text categorization that combines a machine learning algorithm, which provides a base model trained with a labeled corpus, with a rule-based expert system, which is used to improve the results provided by the previous classifier, by filtering false positives and dealing with false negatives. The main advantage is that the system can be easily fine-tuned by adding specific rules for those noisy or conflicting categories that have not been successfully trained. We also describe an implementation based on k-Nearest Neighbor and a simple rule language to express lists of positive, negative and relevant (multiword) terms appearing in the input text. The system is evaluated in several scenarios, including the popular Reuters-21578 news corpus for comparison to other approaches, and categorization using IPTC metadata, EUROVOC thesaurus and others. Results show that this approach achieves a precision that is comparable to top ranked methods, with the added value that it does not require a demanding human expert workload to train

Text categorization methods for automatic estimation of verbal intelligence

In this paper we investigate whether conventional text categorization methods may suffice to infer different verbal intelligence levels. This research goal relies on the hypothesis that the vocabulary that speakers make use of reflects their verbal intelligence levels. Automatic verbal intelligence estimation of users in a spoken language dialog system may be useful when defining an optimal dialog strategy by improving its adaptation capabilities. The work is based on a corpus containing descriptions (i.e. monologs) of a short film by test persons yielding different educational backgrounds and the verbal intelligence scores of the speakers. First, a one-way analysis of variance was performed to compare the monologs with the film transcription and to demonstrate that there are differences in the vocabulary used by the test persons yielding different verbal intelligence levels. Then, for the classification task, the monologs were represented as feature vectors using the classical TF–IDF weighting scheme. The Naive Bayes, k-nearest neighbors and Rocchio classifiers were tested. In this paper we describe and compare these classification approaches, define the optimal classification parameters and discuss the classification results obtained.

Architecture for text normalization using statistical machine translation techniques

This paper proposes an architecture, based on statistical machine translation, for developing the text normalization module of a text to speech conversion system. The main target is to generate a language independent text normalization module, based on data and flexible enough to deal with all situa-tions presented in this task. The proposed architecture is composed by three main modules: a tokenizer module for splitting the text input into a token graph (tokenization), a phrase-based translation module (token translation) and a post-processing module for removing some tokens. This paper presents initial exper-iments for numbers and abbreviations. The very good results obtained validate the proposed architecture.

Language identification based on a discriminative text categorization technique

In this paper, we describe new results and improvements to a lan-guage identification (LID) system based on PPRLM previously introduced in [1] and [2]. In this case, we use as parallel phone recognizers the ones provided by the Brno University of Technology for Czech, Hungarian, and Russian lan-guages, and instead of using traditional n-gram language models we use a lan-guage model that is created using a ranking with the most frequent and discrim-inative n-grams. In this language model approach, the distance between the ranking for the input sentence and the ranking for each language is computed, based on the difference in relative positions for each n-gram. This approach is able to model reliably longer span information than in traditional language models obtaining more reliable estimations. We also describe the modifications that we have being introducing along the time to the original ranking technique, e.g., different discriminative formulas to establish the ranking, variations of the template size, the suppression of repeated consecutive phones, and a new clus-tering technique for the ranking scores. Results show that this technique pro-vides a 12.9% relative improvement over PPRLM. Finally, we also describe re-sults where the traditional PPRLM and our ranking technique are combined.

Architecture for Text Sign Localization and Recognition

This paper describes a low complexity strategy for detecting and recognizing text signs automatically. Traditional approaches use large image algorithms for detecting the text sign, followed by the application of an Optical Character Recognition (OCR) algorithm in the previously identified areas. This paper proposes a new architecture that applies the OCR to a whole lightly treated image and then carries out the text detection process of the OCR output. The strategy presented in this paper significantly reduces the processing time required for text localization in an image, while guaranteeing a high recognition rate. This strategy will facilitate the incorporation of video processing-based applications into the automatic detection of text sign similar to that of a smartphone. These applications will increase the autonomy of visually impaired people in their daily life.

Multilingual number transcription for text-to-speech conversion

This paper describes the text normalization module of a text to speech fully-trainable conversion system and its application to number transcription. The main target is to generate a language independent text normalization module, based on data instead of on expert rules. This paper proposes a general architecture based on statistical machine translation techniques. This proposal is composed of three main modules: a tokenizer for splitting the text input into a token graph, a phrase-based translation module for token translation, and a post-processing module for removing some tokens. This architecture has been evaluated for number transcription in several languages: English, Spanish and Romanian. Number transcription is an important aspect in the text normalization problem.