Many augmentative communication systems employ word prediction to help minimize the number of user actions needed to construct messages. Statistical prediction techniques rely upon a database (model) of word frequencies and inter-word correlations derived from a large text corpus. One potential means to improve prediction is to create a set of models derived from domain-specific corpora, dynami...

Semantic Text Mining and its Application in Biomedical Domain Illhoi Yoo Xiaohua Hu, Ph.D A huge amount of biomedical knowledge and novel discoveries have been produced and collected in text databases or digital libraries, such as MEDLINE, because the most natural form to store information is text. In order to cope with this pressing text information overload, text mining is employed. However, ...

OBJECTIVES With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. METHODS This paper reviews text mining processes in detail and the software tools a...

The acquisition of deep textual semantics is a key issue which significantly improves the performances of elearning, web search and web knowledge services, etc. Though many models have been developed to acquire textual semantics, the acquisition of deep textual semantics is still a challenge issue. Herein, an acquisition model of deep textual semantics is developed to enhance the capability of ...

Text prediction has the potential for facilitating and speeding up the documentation work within health care, making it possible for health personnel to allocate less time to documentation and more time to patient care. It also offers a way to produce clinical text with fewer misspellings and abbreviations, increasing readability. We have explored how text prediction can be used for input of cl...

One of the interesting topics on multimedia domain is concerned with empowering computer in order to speech production. Speech synthesis is granting human abilities to the computer for speech production. Data-based approach and process-based approach are the two main approaches on speech synthesis. Each approach has its varied challenges. Unit-selection speech synthesis and statistical parametr...

Applied Literature, however, does not have literature at its centre. Literature in this domain is a tool to solve problems and achieve goals. Using literature to teach and learn languages, the application of literature to language education, is a very handy example. Health Humanities (by Crawford, et al. and reviewed by A. Ramazani in our Journal's previous issue) comprises chapters on how lite...

This essay is considered an attempt to present how semiotic studies can be used as a perceptional aspect in reading architecture and urbanism. Appearance of each art is similar to creation of a “text” which transfers a set of customs, values and thought together with itself. Production of each “text” is based on its context, culture and intellectual bed of its origin society. Each text is an ...

Natural language systems trained on labeled data from one domain do not perform well on other domains. Most adaptation algorithms proposed in the literature train a new model for the new domain using unlabeled data. However, it is time consuming to retrain big models or pipeline systems. Moreover, the domain of a new target sentence may not be known, and one may not have significant amount of u...

Most supervised language processing systems show a significant drop-off in performance when they are tested on text that comes from a domain significantly different from the domain of the training data. Sequence labeling systems like partof-speech taggers are typically trained on newswire text, and in tests their error rate on, for example, biomedical data can triple, or worse. We investigate t...