نتایج جستجو برای: statistical language model
تعداد نتایج: 2689345 فیلتر نتایج به سال:
the present research was an attempt to see how quranic lexical collocations were translated into english by two professional translators namely, abdullah yusuf(2005), and muhammad s. shakir(2012). the study attempted qualitatively to shed light on how translators dealt with quranic lexical collocations when transferring them to the target language based on the newmark(1988) model , and quantit...
Current video search systems commonly return video shots as results. We believe that users may better relate to longer, semantic video units and propose a retrieval framework for news story items, which consist of multiple shots. The framework is divided into two parts: (1) A concept based language model which ranks news items with known occurrences of semantic concepts by the probability that ...
In this paper, we introduce a new methodology for modeling product aspects from a collection of free-text customer reviews. The proposal relies on a language modeling framework and is domain independent. It combines both a kernelbased model of opinion words and a stochastic translation model between words to approach the aspect model of products. We also present a ranking-based methodology to m...
the purpose of this study is to investigate the relationships between teachers’ immediacy behaviors and iranian students’ willingness to talk in english classes. analysis of the results from willingness to talk scale represents a relatively high level of willingness to talk in english classrooms among iranian language learners. the total mean score of students’ willingness to talk was 66.3 ou...
Parsing is important in Linguistics and Natural Language Processing to understand the syntax and semantics of a natural language grammar. Parsing natural language text is challenging because of the problems like ambiguity and inefficiency. Also the interpretation of natural language text depends on context based techniques. A probabilistic component is essential to resolve ambiguity in both syn...
Currently, N-gram models are the most common and widely used models for statistical language modeling. In this paper, we investigated an alternative way to build language models, i.e., using artificial neural networks to learn the language model. Our experiment result shows that the neural network can learn a language model that has performance even better than standard statistical methods.
Parsing is important in Linguistics and Natural Language Processing to understand the syntax and semantics of a natural language grammar. Parsing natural language text is challenging because of the problems like ambiguity and inefficiency. Also the interpretation of natural language text depends on context based techniques. A probabilistic component is essential to resolve ambiguity in both syn...
This study aimed, firstly, to investigate the underlying components of Iranian cultural identity and, secondly, to confirm the aforementioned components via Structural Equation Modeling (SEM) analysis. In order to achieve these goals, the researchers reviewed the extensive local and international literature on language, culture and identity. Based on the literature and consultations with a grou...
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