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Persons are often asked to provide information about themselves. These data are very heterogeneous and result in as many “profiles” as contexts. Sorting a large amount of profiles from different contexts and assigning them back to a specific individual is quite a difficult problem. Semantic processing and machine learning are key tools to achieve this goal. This paper describes a framework to address...
This paper introduces a series of results and experiments used in the development of a Romanian text-to-speech system, focusing on text statistics. We investigate the presence of several linguistic units used in text-to-speech systems, from phonemes to words. The text corpus we used, News-Romanian (News-RO) comprises 4500 newspaper articles. A subset of it, around 2500 sentences represents the Romanian...
In this paper, the Markov Family Models, a kind of statistical Models was firstly introduced. Under the assumption that the probability of a word depends both on its own tag and previous word, but its own tag and previous word are independent if the word is known, we simplify the Markov Family Model and use for part-of-speech tagging successfully. Experimental results show that this part-of-speech...
Text summarization is a meaningful part of the research of natural language document understanding, and it is an important branch of natural language processing. In this paper, on the basis of the research status quo of the researchers and experts both home and abroad, two text summarization algorithms are proposed. And one algorithm is rule-based, and the other is based on statistics.
Text classification is an active research area in information retrieval and natural language processing. A fundamental tool in text classification is a list of 'stop' words(stop word list) that is used to identify frequent words that are unlikely to assist in classification and hence are deleted during pre-processing. Till now, many stop word lists have been developed for English language. However,...
This article demonstrates a new approach to measuring English readability applying techniques from natural language processing and information theory. We attempt to regard reading process as a process of information processing and transferring. And then we apply statistical language modeling technique and information theory to compute the information of text passage, based on which the readability...
The semantic interpretation of nominal compounds is one of the most difficult problems in natural language processing. VN compound is a subset of nominal compounds where the modifier is a verb nominalization. This paper proposes a new interpretation model in which a support vector machine is applied to label five semantic relations involved in Chinese VN compounds. The World Wide Web is exploited...
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