Keyword-based queries are an important means to retrieve information from XML collections with unknown or complex schemas. Relevance Feedback integrates relevance information provided by a user to enhance retrieval quality. For keyword-based XML queries, feedback engines usually generate an expanded keyword query from the content of elements marked as relevant or nonrelevant. This approach that...

In this research, we propose the string vector based KNN as the approach to the keyword extraction. The keyword extraction may be viewed as an instance of word classification, encoding words into numerical vectors may cause the main problems, such as the huge dimensionality, the sparse distribution and the poor transparency, and the problems were solved by encoding texts into string vectors in ...

this study used keyword method during encoding information in transferring information from short term memory to make the retrieval easier. for this purpose, 50 adult female elementary students were chosen to participate in this study. this study required two groups of learners (control and experimental groups). the experimental group enjoyed some special flashcards which each of them involved ...

We describe a wordspotting algorithm based on a predictive neural model for a telephone speech corpus. Each keyword is modeled as a whole word. For keyword detection scoring we used a minimum accumulated prediction residual. We computed empirically a threshold value for rejecting non-keyword speech in place of building non-keyword models. We tested the algorithm with the TUBTEL telephone speech...

Current peer-to-peer (p2p) full-text keyword search techniques fall into the following categories: document-based partitioning, keyword-based partitioning, hybrid indexing, and semantic search. This paper provides a performance evaluation and comparison of these p2p full-text keyword search techniques on a dataset with 3.7 million web pages and 6.8 million search queries. Our evaluation results...

Keyword spotting deals with the search of a reduced set of keywords in audio content. Phone Lattice-based approaches are very fast but achieve poor results. HMM-based keyword spotting systems deal with filler models to absorb the Out-of-vocabulary (OOV) words and achieve best results although they are slower. We propose a technique which combines them in order to perform a confidence measure to...