Improving Latent Semantic Indexing with concepts mapping based on domain ontology

Document categorization is the process of classifying documents from many mixed documents automatically, and the main problem is how to express document content in vector space completely. This paper proposes a new model named Latent Semantic Analysis (LSA) + word2vec to categorize documents. This is the first attempt of combining word2vec with LSA at document categorization and it can map document to vector space under the premise of keeping document contents fully. At first, we create a term by document matrix and the element of which is decided by Term Frequency-Inverse Document Frequency (TF-IDF) weighting and word vector trained by word2vec. This matrix is a 3-dimensional matrix and it can describe the meaning of every word and the content of every document exactly. Secondly, Singular Value Decomposition (SVD) is executed on the matrix and lower computational complexity is gained from this. The model is named LSA + word2vec. Then, document vector gained from the new model are put into Convolutional Neural Network (CNN) to train. CNN is an efficient deep learning algorithm, which improves the accuracy of classification greatly. We evaluate the performance based on the 20newsgroups corpus. The results show that our new model achieves better effects on document categorization tasks, and the accuracy made about 15% improvement than traditional methods, such as LSA and Vector Space Model (VSM).

In this paper, we propose a method of genetic algorithm (GA) for text clustering based on singular value decomposition technique. The main difficulty in the application of GA to text clustering is its long string representation in high dimensional space. Because the most straightforward and popular approach represents texts with vector space model (VSM), that is, each unique term in the vocabulary represents one dimension. Singular value decomposition (SVD) is a successful technique arising from numerical linear algebra that is used in latent semantic indexing (LSI). Employing the SVD-based document representation, LSI can overcome the problems by using statistically derived conceptual indices instead of individual words and provide a dimension reduced space. Genetic algorithm belongs to search techniques which could automatically exploit the optimal solution for objective or fitness function of an optimization problem. GA can be used in conjunction with the reduced latent semantic structure and improve clustering efficiency and accuracy. Our algorithm is performed on Reuter documents collection. The results show that the performance of SVD-based GA is significantly superior to that of conventional GA in vector space model.

Nowadays, searching for a topic on the Internet can be a frustrating experience because of all the excessive information. Thus, a strategy for automatically classifying the results can improve user experience and work efficiency. Latent Semantic Indexing (LSI) algorithm is used to classify documents by meaning due to its effectiveness. However, there is a problem with the implementation of this algorithm. LSI is computationally intensive because the cost is directly related to the number of documents. In particular, the Singular Value Decomposition (SVD) that is mainly used in LSI is unscalable in terms of both memory and computation time. One possible solution is to use more powerful computational resources, such as multiple computing nodes. In this paper, a novel distributed architecture for the LSI algorithm is proposed. It is based on the use of microservices in a Google Cloud environment. We evaluated the performances of the proposed Cloud-based LSI, and comparison is made with standalone LSI. The results show the benefits of using distributed systems based on runtime, concurrency, and processing.

We present an application of a novel methodology called Text Influenced Molecular Indexing (TIMI) to mine the information in the scientific literature. TIMI is an extension of two existing methodologies: (1) Latent Semantic Structure Indexing (LaSSI), a method for calculating chemical similarity using two-dimensional topological descriptors, and (2) Latent Semantic Indexing (LSI), a method for generating correlations between textual terms. The singular value decomposition (SVD) of a feature/object matrix is the fundamental mathematical operation underlying LSI, LaSSI, and TIMI and is used in the identification of associations between textual and chemical descriptors. We present the results of our studies with a database containing 11,571 PubMed/MEDLINE abstracts which show the advantages of merging textual and chemical descriptors over using either text or chemistry alone. Our work demonstrates that searching text-only databases limits retrieved documents to those that explicitly mention compounds by name in the text. Similarly, searching chemistry-only databases can only retrieve those documents that have chemical structures in them. TIMI, however, enables search and retrieval of documents with textual, chemical, and/or text- and chemistry-based queries. Thus, the TIMI system offers a powerful new approach to uncovering the contextual scientific knowledge sought by the medical research community.

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.

Latent Semantic Analysis (LSA) is a method that allows us to automatically index and retrieve information from a set of objects by reducing the term-by-document matrix using the Singular Value Decomposition (SVD) technique. However, LSA has a high computational cost for analyzing large amounts of information. The goals of this work are (i) to improve the execution time of semantic space construction, dimensionality reduction, and information retrieval stages of LSA based on heterogeneous systems and (ii) to evaluate the accuracy and recall of the information retrieval stage. We present a heterogeneous Latent Semantic Analysis (hLSA) system, which has been developed using General-Purpose computing on Graphics Processing Units (GPGPUs) architecture, which can solve large numeric problems faster through the thousands of concurrent threads on multiple CUDA cores of GPUs and multi-CPU architecture, which can solve large text problems faster through a multiprocessing environment. We execute the hLSA system with documents from the PubMed Central (PMC) database. The results of the experiments show that the acceleration reached by the hLSA system for large matrices with one hundred and fifty thousand million values is around eight times faster than the standard LSA version with an accuracy of 88% and a recall of 100%.

An image data query algorithm based on Latent Semantic Indexing in domain ontology and singular value decomposition is proposed. It projects query expansion vector on latent semantic area. A new method to computing similarity between query vectors and query documents is proposed. Users can get accurate and effective retrieval results using the similarity. The experimental results show that the proposed solution can improve image index query recall ratio and precision ratio.

This paper proposes and method to improve retrieval performance of the vector space model (VSM) by utilizing user-supplied information of those documents that are relevant to the query in question. In addition to the user's relevance feedback information, incorporated into the retrieval model, which is built by using a sequence of linear transformations, is information such as inter-document similarity values. Then, the high-dimensional and sparse vectors are reduced by SVD (Singular Value Decomposition) and transformed into the low-dimensional vector space, namely the space representing the latent semantic meanings of the words. The method was experimented on through two test collections, Medline collection and Cranfield collection. Improvement of average precision compared with LSI (Latent Semantic Indexing) model were 4.03% (Medline) and 24.87% (Cranfield) for the two training data sets, and 0.01% (Medline) and 4.89% (Cranfield) for the test data, respectively. The proposed method provides an approach that makes it possible to preserve the user-supplied relevance information for a long term in the system and to use the information later.

Singular value decomposition is an important method to factorize a matrix, which is useful for many applications, such as principal component analysis in statistics and latent semantic analysis in natural language text processing. In order to diminish the computational complexity, incremental update algorithm is developed, especially for analyzing relationships between text documents. However, it is at the cost of a reduction in precision. In this paper, we study the effect of incremental update on latent semantic analysis; and, by experiments, to investigate the trend downward of computing precision and then try to find the balance point between re-computation and incremental update. The results indicate that when increased documents are less than original documents, incremental update method has an approximate precision to re-computing method, and then its recall ratio will obviously decline. It could be helpful for deciding when the matrix factorization must be recomputed.

QuantNet is an integrated web-based environment consisting of different types of statistics-related documents and program codes. Its goal is creating reproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM) and latent semantic analysis (LSA). The LSA has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between documents. Our results show that different model configurations allow adapted similarity-based document clustering and knowledge discovery. In particular, different LSA configurations together with hierarchical clustering reveal good results under M3 evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under quantlet. The driving technology behind it is Q3-D3-LSA, which is the combination of “GitHub API based QuantNet Mining infrastructure in R”, LSA and D3 implementation.

The exponential growth of data demands scalable infrastructures capable of indexing and searching rich content such as text, music, and images. A promising direction is to combine information re-trieval with peer-to-peer technology for scalability, fault-tolerance, and low administration cost. One pioneering work along this di-rection is pSearch [32, 33]. pSearch places documents onto a peer-to- peer overlay network according to semantic vectors produced using Latent Semantic Indexing (LSI). The search cost for a query is reduced since documents related to the query are likely to be co-located on a small number of nodes. Unfortunately, because of its reliance on LSI, pSearch also inherits the limitations of LSI. (1) When the corpus is large and heterogeneous, LSI's retrieval quality is inferior to methods such as Okapi. (2) The Singular Value Decomposition (SVD) used in LSI is unscalable in terms of both memory consumption and computation time.This paper addresses the above limitations of LSI and makes the following contributions. (1) To reduce the cost of SVD, we reduce the size of its input matrix through document clustering and term selection. Our method retains the retrieval quality of LSI but is several orders of magnitude more efficient. (2) Through extensive experimentation, we found that proper normalization of semantic vectors for terms and documents improves recall by 76%. (3) To further improve retrieval quality, we use low-dimensional subvectors of semantic vectors to cluster documents in the overlay and then use Okapi to guide the search and document selection.

Latent semantic analysis (LSA) usually uses the singular value decomposition (SVD) of the term-document matrix for discovering the latent relationships within the document collection. With the SVD, by disregarding the smaller singular values of the term-document matrix a vector space cleaned from noises that distort the meaning is obtained. The latent semantic structure of the terms and documents is obtained by examining the relationship of representative vectors in the vector space. However, the computational time of re-computing or updating the SVD of the term-document is high when adding new terms and/or documents to pre-existing document collection. Thus, the need a method not only has low computational complexity but also creates the correct semantic structure when updating the latent semantic structure is arisen. This study shows that the truncated ULV decomposition is a good alternative to the SVD in LSA modelling about cost and producing the correct semantic structure.

Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding ‘latent semantic analysis’ recent approaches like ‘word2vec’ or ‘node2vec’ are well established tools in this realm. In the present paper we add to this line of research by introducing ‘fca2vec’, a family of embedding techniques for formal concept analysis (FCA). Our investigation contributes to two distinct lines of research. First, we enable the application of FCA notions to large data sets. In particular, we demonstrate how the cover relation of a concept lattice can be retrieved from a computationally feasible embedding. Secondly, we show an enhancement for the classical node2vec approach in low dimension. For both directions the overall constraint of FCA of explainable results is preserved. We evaluate our novel procedures by computing fca2vec on different data sets like, wiki44 (a dense part of the Wikidata knowledge graph), the Mushroom data set and a publication network derived from the FCA community.KeywordsVector space embeddingCovering relationLink predictionComplex dataWord2VecFormal concept analysisClosed setsLow dimensional embedding

In this chapter, we introduce latent semantic analysis (LSA), which uses singular value decomposition (SVD) to reduce the dimensionality of the document-term representation. This method reduces the large matrix to an approximation that is made up of fewer latent dimensions that can be interpreted by the analyst. Two important concepts in LSA, cosine similarity and queries, are explained. Finally, we discuss decision-making in LSA.

A framework for understanding Latent Semantic Indexing (LSI) performance