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In this paper we develop an approach to automatic, data-driven generation of pronunciation dictionaries for keyword spotting(KWS) systems. In practical applications, KWS tasks often have to deal with keywords whose pronunciations can not be found in the dictionary. To solve this problem, we study how to derive
Keyword spotting is the task of detecting keywords of interest in continuous speech. This work investigates the application of keyword spotting to detect crime and it can be used along with telephone tapping and audio monitoring devices by security organization. In this work phonetic based word spotter is developed. A
data is a matter of concern and especially when cloud storage service is used. Encryption is the most effective way to achieve data security. Sensitive data have to be encrypted before outsourcing in spite of the fact that, retrieval of encrypted data becomes an intriguing task. Although various searching techniques are
As Cloud Computing becomes popular, more and more data owners prefer to store their data into the cloud for great flexibility and economic savings. In order to protect the data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a challenging task. Although
Online underground economy is an important channel that connects the merchants of illegal products and their buyers, which is also constantly monitored by legal authorities. As one common way for evasion, the merchants and buyers together create a vocabulary of jargons (called "black keywords" in this
To support understanding of news, we propose a novel TEC model (Topic-Event Causal relation model) and describe the method to construct a Causal Network in the TEC model. The model includes two types of keywords to represent casual relations: topic keywords, which describe topics, and event keywords, which describe
duplicate content also makes some traditionally difficult vision tasks become simple and easy. For example, annotating pictures can be as simple as recycling the tags of Internet images retrieved from image search engines. Such tasks, of either to eliminate or to recycle near-duplicates, can usually be achieved by the nearest
Emotion recognition has become an important topicin natural language processing. Usually words labeled with theiremotion is the starting place to find the emotion of a text, but itis very essential that context must also be considered. It is not asimple task to capture the overall emotion of a Text, as words
This paper presents an integrated approach to automatically provide an overview of content on Thai websites based on tag cloud. This approach is intended to address the information overload issue by presenting the overview to users in order that they could assess whether the information meets their needs. The approach
Nowadays the famous search engine companies are all providing the keyword web search capabilities. No one provides the high accurate & efficient user-requirements-oriented information Services. The task-focused massive multi-source heterogeneous information sharing & utilizing method and system is introduced
Keyword extraction problem is one of the most significant tasks in information retrieval. High-quality keyword extraction sufficiently influences the progress in the following subtasks of information retrieval: classification and clustering, data mining, knowledge extraction and representation, etc. The research
In this paper, we present the system of automatic MCQs (Multiple Choice Questions) generation for any given input text along with a set of distractors. The system is trained on a Wikipedia-based dataset consisting of URLs of Wikipedia articles. The important words (keywords) which consist of both bigrams and unigrams
taken into account when indexing documents and when performing searching. Utilizing this approach, it is possible to use a natural language to express user queries. In many cases, this way is more usual for users to describe their information needs compared to the keyword style. The factoid question answering task is one
student's answer against an answer-key that comprises key phrases of the solution and alternatives, if any. We rely on five algorithms from literature on natural language processing to assess various aspects of a student's answer, such as the quantity and extent of match between keywords in a student's answer and the answer
Research proposal grouping is one of the most important tasks for research project selection in research funding agencies. In this paper, a novel ontology based frequent item set method is proposed to deal with research proposal grouping problem. In the proposed method, a research ontology is firstly constructed to
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