Toward a multi-sensor neural net approach to automatic text classification

Text representation, which is a fundamental and necessary process for text-based intelligent information processing, includes the tasks of determining the index terms for documents and producing the numeric vectors corresponding to the documents. In this paper, multi-word, which is regarded as containing more contextual semantics than individual word and possessing the favorable statistical characteristics, is proposed as an alternative index terms in vector space model for text representation with theoretical support. We investigate the traditional indexing methods as TF*IDF (term frequency inverse document frequency) and LSI (latent semantic indexing) for comparative study. The performances of TF*IDF, LSI and multi-word are examined on the tasks of text classification, which includes information retrieval (IR) and text categorization (TC), in Chinese and English document collection respectively. We also attempt to tune the rescaling factor of LSI and observe its effectiveness in text classification. The experimental results demonstrate that TF*IDF and multi-word are comparable when they are used for IR and TC and LSI is the poorest one of them. Moreover, the rescaling factor of LSI has an insignificant influence on its effectiveness on text classification for both Chinese and English text classification.

We propose a novel method for automatic annotation, indexing and annotation-based retrieval of images. The new method, that we call Markovian Semantic Indexing (MSI), is presented in the context of an online image retrieval system. Assuming such a system, the users' queries are used to construct an Aggregate Markov Chain (AMC) through which the relevance between the keywords seen by the system is defined. The users' queries are also used to automatically annotate the images. A stochastic distance between images, based on their annotation and the keyword relevance captured in the AMC, is then introduced. Geometric interpretations of the proposed distance are provided and its relation to a clustering in the keyword space is investigated. By means of a new measure of Markovian state similarity, the mean first cross passage time (CPT), optimality properties of the proposed distance are proved. Images are modeled as points in a vector space and their similarity is measured with MSI. The new method is shown to possess certain theoretical advantages and also to achieve better Precision versus Recall results when compared to Latent Semantic Indexing (LSI) and probabilistic Latent Semantic Indexing (pLSI) methods in Annotation-Based Image Retrieval (ABIR) tasks.

Latent semantic indexing (LSI) has been shown to be extremely useful in information retrieval, but it is not an optimal representation for text classification. It always drops the text classification performance when being applied to the whole training set (global LSI) because this completely unsupervised method ignores class discrimination while only concentrating on representation. Some local LSI methods have been proposed to improve the classification by utilizing class discrimination information. However, their performance improvements over original term vectors are still very limited. In this paper, we propose a new local LSI method called "local relevancy weighted LSI" to improve text classification by performing a separate single value decomposition (SVD) on the transformed local region of each class. Experimental results show that our method is much better than global LSI and traditional local LSI methods on classification within a much smaller LSI dimension.

In text classification, one key problem is its inherent dichotomy of polysemy and synonym; the other problem is the insufficient usage of abundant useful, but unlabeled text documents. Targeting on solving these problems, we incorporate a sprinkling Latent Semantic Indexing (LSI) with background knowledge for text classification. The motivation comes from: 1) LSI is a popular technique for information retrieval and it also succeeds in text classification solving the problem of polysemy and synonym; 2) By fusing the sprinkling terms and unlabeled terms, our method not only considers the class relationship, but also explores the unlabeled information. Finally, experimental results on text documents demonstrate our proposed method benefits for improving the classification performance.

A text classification model based on Latent Semantic Analysis and Improved Hyper-sphere Support Vector Machine, is proposed in order to improve the accuracy and efficiency of text classification. Latent Semantic Analysis is used in this model for feature extraction, eliminating the text representation errors caused by synonyms and polysemes, and reducing the dimension of text vector. At the same time, a new decision-making approach based on concentration, is designed for improving the classification of texts in overlapping regions. Experimental results show that the model will give good classification results when the number of classes is small. And the improved algorithm is effective and feasible.

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.

We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from Latent Semantic Indexing (LSI) which is optimal in the sense of global Euclidean structure, LPI is optimal in the sense of local manifold structure. However, LPI is not efficient in time and memory which makes it difficult to be applied to very large data set. Specifically, the computation of LPI involves eigen-decompositions of two dense matrices which is expensive. In this paper, we propose a new algorithm called Regularized Locality Preserving Indexing (RLPI). Benefit from recent progresses on spectral graph analysis, we cast the original LPI algorithm into a regression framework which enable us to avoid eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizers can be naturally incorporated into our algorithm which makes it more flexible. Extensive experimental results show that RLPI obtains similar or better results comparing to LPI and it is significantly faster, which makes it an efficient and effective data preprocessing method for large scale text clustering, classification and retrieval.

With the recent increase in the number of news videos and in their value, their automatic indexing has been anticipated. Among various proposals for this problem, the use of textual data has been actively considered, and some applications of this type have actually been developed. Nonetheless, most of these approaches are textual data oriented, and a few of them consider the correspondences between their indexes (keywords) and image contents which are essentials for an image database.This paper proposes an automatic video indexing method which takes advantage of the characteristic of news videos: similar images have similar contents. The method tags indexes selectively according to typical image content. The method has been applied to actual news videos, and indexing has been successful for between 25% and 93% of “typical shots,” and for between 75% and 100% of “captionized shots.” © 2001 Scripta Technica, Syst Comp Jpn, 32(9): 32–41, 2001

Automatic music genre classification is one of the most challenging problems in music information retrieval and management of digital music database. In this paper, we propose a new framework using text category methods to classify music genres. This framework is different from current methods for music genre classification. In our framework, we consider music as text-like semantic music document, which is represented by a set of music symbol lexicons with a HMM (hidden Markov models) cluster. Music symbols can be seemed as high-level features or semantic features like beats or rhythms. We use latent semantic indexing (LSI) technique that is widely adopted in text categorization for music genre classification. From the experimental results, we could achieve an average recall over 70% for ten musical genres

Internet is becoming an integral part of everyday life and email has become a powerful tool to exchange ideas and information. Through email users connect socially and commercially. But mass mailing i.e. Spam has become one of the biggest worldwide problem. So there is requirement to develop a spam filter which can detect the spam and can stop the mass mailing. In this paper, Latent Semantic Indexing (LSI) approach is used for spam filtering. Latent semantic indexing (LSI) is an indexing and retrieval method which uses singular value decomposition (SVD) to find the relationships between the terms and unstructured collection of message. LSI is based on the principle that terms that are used in the same contexts tend to have similar meanings. Apart from this spam are obtained more or less abundant replications in a significant numbers. The detection of these duplicates is important because it allows to lighten email box and will improve the efficiency of spam filter. The Duplicate Message Detection Techniques (DMDT) has been used to improve the efficiency and effectiveness of spam filter.

The standard application of Latent Semantic Indexing (LSI), a well-known technique for information retrieval, requires the computation of a partial Singular Value Decomposition (SVD) of the term-document matrix. This computation becomes infeasible for large document collections, since it is very demanding both in terms of arithmetic operations and in memory requirements. This paper discusses two divide and conquer strategies, with the goal of alleviating these difficulties. Both strategies divide the document collection in subsets, perform relevance analysis on each subset, and conquer the analysis results to form the query response. Since each sub-problem resulting from the recursive division process has a smaller size, the processing of large scale document collections requires much fewer resources. In addition, the computation is highly parallel and can be easily adapted to a parallel computing environment. To reduce the computational cost, we perform the analysis on the subsets by using the Lanczos vectors instead of singular vectors as in the standard LSI method. This technique is far more efficient than the computation of the truncated SVD, while its accuracy is comparable. Experimental results confirm that the proposed divide and conquer strategies are effective for information retrieval problems.

The selection of an interesting blog is becoming ever increasingly tough work for most users as the number of blogs is exponentially increasing every day. A Recommender System (RS) can help the user to find out the blog of his choice or based on parameters specified by him. The accuracy of RS to find out the right blogs for a particular user gets enhanced, if the text of blog is analyzed using semantic analysis. The RS uses the classification of blogs to find out the right blogs for a user. The information with respect to blogs like blog's title, tags given by blogger, blog image etc., provides good information to classify the blogs. A classification of text can be done using Part of Speech (POS) text analyzer. POS tagger of text provides information about the blogs that can be used to find the similarity between two blogs. The similar types of blogs can be kept together to provide better indexing in the set of large blogs. The paper finds the better accuracy of Collaborative Filtering Recommender System (CF RS) for bloggers when the blogs are processed with different text analysis using semantic information of blogs. The observation is addressed by computing various measures like Precision, Recall and F1.

Comparing and combining the effectiveness of latent semantic indexing and the ordinary vector space model for information retrieval

We view a variety of established methods for ranked retrieval from a common angle, namely as a process of combining query-independent rankings that were precomputed for certain attributes. Apart from a general insight into what effectively distinguishes various schemes from each other, we obtain three specific results concerned with concept-based retrieval. First, we prove that latent semantic indexing (LSI) can be implemented to answer queries in time proportional to the number of words in the query, which improves over the standard implementation by an order of magnitude; a similar result is established for LSI's probabilistic sibling PLSI. Second, we give a simple and precise characterization of the extent, to which latent semantic indexing (LSI) can deal with polysems, and when it fails to do so. Third, we demonstrate that the recombination of the intricate, yet relatively cheap mechanism of PLSI for mapping queries to attributes, with a simplistic, easy-to-compute set of document rankings gives a retrieval performance which is at least as good as that of the most sophisticated concept-based retrieval schemes and which does not require any precomputation

This paper provides a broad overview of three popular textual semantic classification algorithms used both in the industry and in the scientific community. The three algorithms are TF-IDF, Latent Semantic Analysis and Latent Dirichlet Allocation. We selected these three algorithms because they are the foundation of semantic classification and they are still widely used in the field of semantic classification. Firstly, this paper exhibits the inner workings of each of the algorithm both in the original authors intuition and the mathematical model utilized. Next, we discuss the advantages of each of the algorithms based on recent and credible research papers and articles. We also critically dissect the limitations of each of the algorithms. Lastly, we provide a general argument on the way forward in improving of the algorithms. This paper aims to give a general understanding on these algorithms which we hope will spur more research in improving the field of semantic classification.