Latent Semantic Concepts Research Articles

Effective News Text Summarization Techniques

n proposed work, we successfully implemented a news text summarization system using Natural Language Processing (NLP) techniques and the Latent Semantic Analysis (LSA) algorithm. The purpose of our project was to extract important information from a large volume of news articles and present it in a concise and easily understandable manner. To achieve this, we utilized the LSA algorithm, which is known for its ability to capture the underlying semantic structure of text. LSA employs a mathematical model to analyse relationships between words in a document, creating a semantic representation where words with similar contexts are grouped together in a vector space. The LSA-based summarization process involved several steps. First, we pre-processed the news articles by removing stop words, punctuation, and other non-relevant elements. Then, we constructed a term-document matrix, where rows represented words and columns represented documents, with matrix values representing word frequencies. Next, we applied Singular Value Decomposition (SVD) to the term-document matrix. SVD helped reduce the matrix's dimensionality by identifying the most important latent semantic concepts. This resulted in a lower-dimensional representation that captured the essential information. Finally, we identified the most important sentences in the news articles by measuring the cosine similarity between each sentence and the summary. Sentences with the highest cosine similarity scores were selected as summary sentences. The proposed system demonstrated the effectiveness of the LSA algorithm for news text summarization. By capturing the semantic structure of the text, it generated summaries that allowed users to understand the key points of a news article quickly and easily. Our implementation had practical applications for content recommendation systems, news aggregation platforms, and personalized news feeds. However, it is important to acknowledge the limitations of the LSA algorithm. It may struggle with handling idiomatic expressions and can be sensitive to the quality of the input data. These considerations highlight the need for ongoing research and development to enhance the performance and robustness of news text summarization systems.

Bayesian Cnn Modeling Based on Latent and High-Level Semantic Concepts for Automatic Image Description

Bayesian Cnn Modeling Based on Latent and High-Level Semantic Concepts for Automatic Image Description

Computing semantic relatedness using latent semantic analysis and fuzzy formal concept analysis

Computing semantic relatedness using latent semantic analysis and fuzzy formal concept analysis

Latent semantic concept regularized model for blind image deconvolution

Blind image deconvolution refers to the recovery of a sharp image when the degradation processing is unknown. Many existing methods have the problem that they are designed to exploit low level image descriptors (e.g. image pixels or image gradient) only, rather than high-level latent semantic concepts, thus there is no guarantee of human visual perception. To address this problem, in this paper, a latent semantic concept regularized (LSCR) method is proposed to reduce the blind deconvolution problem at a semantic level. The proposed method explores the relationship between different image descriptors and exploits sparse measure to favor sharp images over blurry images. And matrix factorization is introduced to learn the latent concepts from the image descriptors. Then, the image prior can be described and constrained by the learned latent semantic concepts of image descriptors using a much more effective convolution matrix. In this case, the blind deconvolution problem can be regularized and the sharp version of the blurry image can be recovered at a new latent semantic level. Furthermore, an iterative algorithm is exploited to derive optimal solution. The proposed model is evaluated on two different datasets, including simulation dataset and real dataset, and state-of-the-art performance is achieved compared with other methods.

Specific video identification via joint learning of latent semantic concept, scene and temporal structure

In this paper, based on three typical characteristics of specific videos, i.e., the theme, scene and temporal structure, a novel data-driven identification architecture for the specific video is proposed. To be concrete, at the frame-level, semantic features and scene features from two independent Convolutional Neural Networks (CNNs) are extracted. At the video-level, Vector of Locally Aggregated Descriptors (VLAD) is firstly adopted to encode spatial representation, and then multiple-layer Long Short-Term Memory (LSTM) networks are introduced to represent temporal information. Additionally, a large-scale specific video dataset (SVD) is built for evaluation. The experimental results show that our method obtain impressive 98% mAP. Moreover, in order to validate generalization capability of proposed architecture, extensive experiments on two public datasets, Columbia Consumer Videos (CCV) and Unstructured Social Activity Attribute (USAA), are conducted. Comparison results indicate that our approach outperforms state-of-the-art methods on USAA, and achieves comparable results on CCV.

Analyzing semantic correlation for cross-modal retrieval

With the development of multimedia technology, effective cross-modal retrieval methods are increasingly demanded. The key point of cross-modal retrieval is analyzing the correlation of heterogeneous modalities. There are mainly two types of correlation: content correlation and semantic correlation. Semantic correlation is constructed at a high level of abstraction which is more close to the human understanding than content correlation. In this paper, we investigate a semantic model to construct the semantic correlation for cross-modal retrieval. We assume that the semantic correlation of multimedia data from different modalities can be conditionally generated by semantic concepts in a probabilistic generation framework. The cross-modal semantic generation model (CMSGM) is proposed based on this assumption. We consider three cases of the cross-modal retrieval task. The first is the ideal case that all manifest concepts exist in training data for constructing the correlation, and we propose manifest CMSGM (M-CMSGM) which directly uses CMSGM on the manifest semantic concepts for retrieval. The second is the case that there are no manifest concepts in training data, and latent CMSGM (L-CMSGM) based on latent semantic concepts is proposed for this case, where the latent semantic concepts are learned by asymmetric spectral clustering. The last is the most general case that some of the manifest concepts exist, and we combine M-CMSGM and L-CMSGM to get combinative CMSGM (C-CMSGM) to solve this case. Experimental results on Wikipedia featured articles and MIR Flickr show that our methods have better performance compared with previous state-of-the-art methods. And C-CMSGM can maintain good performance in the case that manifest concepts are lacking, which confirms the robustness and practicality of C-CMSGM.

Satellite Recognition via Sparse Coding Based Probabilistic Latent Semantic Analysis

Traditional satellite recognition usually applies low-level features (e.g., invariant moment) to describe the global information of satellites. Therefore, the local property and the latent high-level semantic concepts of satellites are likely to be ignored. Recently, the topic model has been given more attention in object recognition field, which generally supposes that the local feature can be modeled as words and images are regarded as documents. Based on this assumption, it aims to discover the latent topics existed between words and documents and then utilizes the topics to represent the images. However, topic model often uses bag-of-words (BoW) strategy that each local feature descriptor is represented by only one word. In this paper, we propose a novel method called sparse coding (SC) based probabilistic latent semantic analysis (SC-pLSA) for satellite recognition. Compared with conventional topic models, our method performs sparse coding to explore the potential correlation between the descriptor and multiple words. Consequently, the descriptor can be described by a small set of words. Based on this word-level representation, probabilistic latent semantic analysis (pLSA) model with simplicity and low computational cost is employed for learning the latent topics. Experimental results on the BUAA-SID 1.0 multi-view satellite dataset validate the effectiveness of our proposed method, and especially under the interference of noise, it outperforms the traditional recognition methods: Hu invariant moment, BoW, SC, locality-constrained linear coding (LLC), conventional pLSA and latent Dirichlet allocation (LDA) with three different classifiers: nearest neighbor (NN) classifier, linear SVM classifier, and sparse representation classifier (SRC).

Predicting e-commerce company success by mining the text of its publicly-accessible website

We analyze the impact of textual information from e-commerce companies’ websites on their commercial success. The textual information is extracted from web content of e-commerce companies divided into the Top 100 worldwide most successful companies and into the Top 101 to 500 worldwide most successful companies. It is shown that latent semantic concepts extracted from the analysis of textual information can be adopted as success factors for a Top 100 e-commerce company classification. This contributes to the existing literature concerning e-commerce success factors. As evaluation, a regression model based on these concepts is built that is successful in predicting the commercial success of the Top 100 companies. These findings are valuable for e-commerce websites creation.

Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing

We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts from textual information of existing customers’ websites can be used to uncover characteristics of websites of companies that will turn into profitable customers. Hence, the use of predictive analytics will help to identify new potential acquisition targets. Additionally, we show that a regression model based on these concepts is successful in the profitability prediction of new customers. In a case study, the acquisition process of a mail-order company is supported by creating a prioritized list of new customers generated by this approach. It is shown that the density of profitable customers in this list outperforms the density of profitable customers in traditional generated address lists (e.g. from list brokers). From a managerial point of view, this approach supports the identification of new business customers and helps to estimate the future profitability of these customers in a company. Consequently, the customer acquisition process can be targeted more effectively and efficiently. This leads to a competitive advantage for B2B companies and improves the acquisition process that is time- and cost-consuming with traditionally low conversion rates.

Image Retrieval with Generative Model for Typicality

One of the most common image retrieval tasks is to find the most typical image that depicts the object specified by a query. Existing image search engines cannot efficiently do this since their search results are often a mixture of images belonging to various semantic concepts. We therefore introduce a probabilistic model for typicality. Our model consists of images, symbolic features, and latent semantic concepts (aspects). The aspect with highest probability is assumed to represent typicality. By collecting a large number of images, we can estimate parameters using EM algorithm. The estimated parameters are used to quantify the level of typicality for each image. Based on the proposed method, we have implemented a system, for ranking images by their typicality. Experiments using both artificial and real data showed the effectiveness of our method.

Query representation by structured concept threads with application to interactive video retrieval

In this paper, we provide a new formulation for video queries as structured combination of concept threads, contributing to the general query-by-concept paradigm. Occupying a low-dimensional region in the concept space, concept thread defines a ranked list of video documents ordered by their combined concept predictions. This localized representation incorporates the previous concept based formulation as a special case and extends the restricted AND concept combination logic to a two-level concept inference network. We apply this new formulation to interactive video retrieval and utilize abundant feedback information to mine the latent semantic concept threads for answering complex query semantics. Simulative experiments which are conducted on two years’ TRECVID data sets with two sets of concept lexicons demonstrate the advantage of the proposed formulation. The proposed query formulation offers some 60% improvements over the simple browsing search baseline in nearly real time. It has clear advantages over c-tf-idf and achieves better results over the state-of-the-art online ordinal reranking approach. Meanwhile, it not only alleviates user’s workload significantly but also is robust to user mislabeling errors.