Lexical disambiguation in machine translation with latent semantic analysis

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%.

The use of machine translation as artificial intelligence (AI) keeps increasing and the world’s most popular translation tool is Google Translate (GT). This tool is not merely used for the benefits of learning and obtaining information from foreign languages through translation but has also been used as a medium of interaction and communication in hospitals, airports and shopping centers. This paper aims to explore machine translation accuracy in translating French-Indonesian culinary texts (recipes). The samples of culinary text were taken from the internet. The research results show that the semiotic model of machine translation in GT is the translation from the signifier (forms) of the source language to the signifier (forms) of the target language by emphasizing the equivalence of the concept (signified) of the source language and the target language. GT aids to translate the existing French-Indonesian culinary text concepts through words, phrases and sentences. A problem encountered in machine translation for culinary texts is a cultural equivalence. GT machine translation cannot accurately identify the cultural context of the source language and the target language, so the results are in the form of a literal translation. However, the accuracy of GT can be improved by refining the translation of cultural equivalents through words, phrases and sentences from one language to another.

The integration of machine translation (MT) and artificial intelligence (AI) has significantly enhanced the precision of MT systems producing translations that rival the quality of skilled human translators. This innovation has broadened the applicability of AI-enabled MT systems attracting users for a variety of purposes. This paper explores the following areas: the application of AI-enabled MT, specifically Google Translate (GT) in English as a Foreign Language (EFL) classroom. Empirical findings and recent research indicate that students are increasingly reliant on MT particularly in higher educational settings where English is not their first language. The purpose of this research is to provide insight on the practices, beliefs and goals of EFL learners who use GT in the classroom. Data was gathered from 234 university students through a questionnaire along with pre- and post-tests to compare the quality of writing drafts created with and without the aid of GT. The findings are consistent with those of previous research conducted worldwide highlighting the immense potential of AI-enabled MT in not only boosting students' learning experiences but also cultivating more independent learners. The study also indicates that students have positive attitudes towards GT and employ diverse search strategies to address various language-related challenges.

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.

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.

ldquoCurse of dimensionalityrdquo is a common problem in the area of information retrieval. It was verified that points in a vector space are projected to a random subspace of suitably high dimension, and then the distances between the points are approximately preserved. Although such a random projection can be used to reduce the dimension of the document space, it does not bring together semantically related documents. Latent Semantic Indexing (LSI) projects documents to lower dimensional LSI space from higher dimensional term space with singular-value decomposition (SVD) for the purpose of reducing the dimensions of the document space and bringing together semantically related documents. But the computation time of SVD is a bottleneck because of the higher dimensions of documents. In this paper, a novel method of dimension reduction for improving LSI is provided. A term-to-concept projection matrix based on domain ontology was created in this method. This way documents were projected to lower dimensional concept space by the projection matrix. LSI pre-computation was performed not on the original term by document matrix, but on the lower dimensional concept by document matrix at great computational savings. Experiments indicate that this method improves the efficiency of LSI. And the similarity judgment between documents is not disturbed.

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) is a generalized vector space method that uses dimension reduction to generate term correlations for use during the information retrieval process. We hypothesized that even though the dimension reduction establishes correlations between terms, the dimension reduction is causing a degradation in the correlation of a term to itself (self-correlation). In this article, we have proven that there is a direct relationship to the size of the LSA dimension reduction and the LSA self-correlation. We have also shown that by altering the LSA term self-correlations we gain a substantial increase in precision, while also reducing the computation required during the information retrieval process.

The present study seeks to address the emotional lexical units in the context of Turkish synonyms that fall under the categories of terms for pain, joy, and excitement, in addition to examining potential English equivalents based on two machine translation tools: Google Translate and DeepL. Additionally, the researchers sought to identify whether two distinct machine translation systems presented near-synonyms in the same way, whether machine translation algorithms considered context when translating emotive lexical units, and how DeepL and Google Translate translated emotive lexical units differently. In this case, Google Translate and DeepL were used to assess sentences from the TDK online website that covered each category and had a total word count of 25. The findings have demonstrated that the machine translation systems DeepL and Google Translate typically produce translations that are comparable, that Google Translate platform considers context more when translating synonyms, and that Google Translate also considers words that are specific to Turkish or words borrowed from Arabic, Persian, and Ottoman Turkish, whereas DeepL occasionally failed in this area. Another finding is that machine translation tools have advanced to a great level, considering near-synonym translation. It is recommended that scholars benefit from the near-synonym corpora gathered in other subjects and their detections in other language pairs and that future research assess synonyms in other fields using machine translation or in various settings.

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

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.

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).

Purpose This study aimed to analyze the readability of Google Translate (GT) and Yandex Translate (YT) translation results on dialogue texts containing cultural terms from the book Antologi Cerita Anak Indonesia (ACAI). This study evaluated the effectiveness of the Neural Machine Translation (NMT) approach in GT and the Hybrid Machine Translation (HMT) approach in YT in conveying text meanings clearly and comprehensibly to readers.Method This research employed a cloze test involving 28 participants aged 18-24 years, along with a questionnaire to assess user preferences regarding GT and YT translation results. Text readability was analyzed using the Flesch-Kincaid Grade Level and Gunning Fog Index to measure the linguistic complexity of the translations.Results/Findings The study results show that GT's readability reaches 81.1%, while YT's readability is 74.5%, both categorized as the independent level according to Rankin & Culhane's (1969) criteria. Additionally, 80% of the 20 questionnaire respondents stated that GT's translations were clearer than those of YT. Analysis using the Flesch-Kincaid Grade Level and Gunning Fog Index shows that the readability level of GT and YT translations is classified as advanced suitable for readers with a minimum education level equivalent to a bachelor's degree.Conclusion This study showed that GT has a higher readability level than YT, which might be because of its use of NMT, producing more natural sentence structures. Meanwhile, YT, which also relied on SMT, translates based on statistical patterns, making its translations more rigid. Although both systems could produce comprehensible translations, they still struggled with accurately translating cultural terms without additional context. Therefore, human involvement remained essential to improving accuracy and contextual appropriateness in machine translation.