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In this paper, we tackle the problem of automatic keyword extraction in the meeting domain, a genre significantly different from written text. For the supervised framework, we proposed a rich set of features beyond the typical TFIDF measures, such as sentence salience weight, lexical features, summary sentences, and
We use query-by-example keyword spotting (QbyE-KWS) approach to solve the personalized wake-up word detection problem for small-footprint, low-computational cost on-device applications. QbyE-KWS takes keywords as templates, and matches the templates across an audio stream via DTW to see if the keyword is included. In
Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, but properties of features have not been well investigated. In most cases, a group of
This work presents a type of method to process automatic summarization. And the method is a kind of trainable summarizer, in which the several characteristics considered such as sentence position, positive keyword, the center of negative keyword, title with similar sentence, sentence included in name entity, sentence
segmentation. A keyword is represented by concatenating its character models. We propose and compare two systems: a script identifier based (IDB) and a script identifier free (IDF) system. IDB uses a HMM based script identifier before spotting a keyword. While, IDF does the spotting without the script identification. The system
engine like Solr and Elastic Search to perform boolean search effectively. This problem is a vital cog in the wheel of text analytics world. It can also be extended to improvise the result of keyword extraction, abstractive summarization, and POS parser tree.
Technological developments have lead to the propagation of massive amounts of data in the form of text, image, audio, and video. The unstoppable trend draws researchers' attention to develop approaches to efficiently retrieve and manage multimedia data. The inadequacy of keyword-based search in multimedia data
Content-based image retrieval systems can automatically extract visual content of images which allow users to query images by their low-level features (such as color and texture). However, users usually prefer querying images based on high-level concepts such as keywords. Classifying images into a number of categories
number of main keywords (5 inputs) each of which has 4 synonyms based on specific constraints. These inputs have been processed by developing two general models including; Artificial Neural Network Back-propagation optimization technique and Subtractive Clustering technique. Furthermore a third general model have developed
The intention of image retrieval systems is to provide retrieved results as close to users' expectations as possible. However, users' requirements vary from each other in various application scenarios for the same concept and keywords. In this paper, we introduce a personalized image retrieval model driven by users
knowledge from existing knowledge bank by extracting linguistic information such as part-of-speech and co-occurrence of keywords and constructing a new domain-adaptive transfer knowledge bank. Through experiments on homogeneous and heterogeneous feature spaces, we testify the efficacy of our methods.
learning approach. We use a graphical model, Dynamic Conditional Random Fields (DCRFs), for training our classifier. Our approach is based on semantic analysis of text to classify the predicates describing coexpression relationship rather than detecting the presence of keywords. We compared our results of sentence
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