Multilingual Information Research Articles

Strategies and Measures to Strengthen the Health Literacy of Migrants: An Online Survey of Primary Care Providers in Hessen

Adequate health literacy is crucial for active participation in health-related decisions. Migrants are one of the population groups that can have more difficulties in finding and using relevant information to make their own decisions. Primary care providers in Germany are the main point of contact for health and disease-related questions and can therefore make an important contribution to strengthening the health literacy of this population group. It remains unclear which specific approaches they use for that purpose. This study aimed to examine which strategies and measures primary care providers in Hessen, Germany, use to strengthen the health literacy of migrants and which additional measures of support they consider important for that purpose. 2,784 primary care providers in Hessen were invited by email between August 1, 2023 and October 24, 2023 to take part in a quantitative online survey. The data were analyzed descriptively. The majority of respondents used multilingual information material, referred to reliable sources of information and contact persons and predominantly used native-speaking staff or digital translation tools to improve the health literacy of migrants. Resources to improve communication and information and an increase in staff were considered by healthcare providers to be helpful in promoting the health literacy of those affected. At the same time, it was emphasized that efforts should also be made on the part of migrant patients to acquire health literacy. Although primary care providers already use strategies to support migrants in communication and decision-making, there is still room for improvement by taking steps to improve access and navigation within the doctor's office, by increasing collaboration with other stakeholders, and by integrating health literacy into the organizational structures of the office.

Demystifying multilingual LIS journals in Scopus: Languages, coverage, metrics, and implications

This study aimed to investigate the languages, countries, geographic distribution, and commonly used journal metrics of multilingual Library and Information Science (LIS) journals indexed in Scopus. The Scimago Journal Rank (SJR) portal was used as a proxy for Scopus-indexed LIS journals. Through examination of the homepages of 249 LIS journals listed in SJR, 42 multilingual LIS journals were identified. For this study, multilingual journals were defined as those publishing in a combination of English and other languages. The findings revealed that although English-only journals have historically dominated the field, a significant number of multilingual journals have been indexed in Scopus over the past two decades. The 42 identified multilingual journals span 25 countries and encompass 18 languages, reflecting a diverse linguistic landscape in LIS scholarship. Notably, 67% are Open Access (OA) and affiliated with major publishers, including university presses. Spain and Brazil emerge as key players, accounting for 27% of all identified multilingual journals, highlighting the importance of their regional languages in LIS scholarship. However, the study also uncovered disparities in journal metrics and rankings, with multilingual journals consistently scoring lower than their English-only counterparts. Despite efforts to include regional and local journals in major databases, these results underscore the persistent challenges multilingual publications face in achieving and maintaining Scopus indexing.

Joint Entity and Relation Extraction Using Machine Reading Comprehension for Urdu

Joint Entity and Relation Extraction (JERE) plays an important role in natural language processing (NLP) by identifying names, locations, and the relationships among them from unstructured text. Despite extensive research in languages like English, JERE poses significant challenges in low-resource languages, particularly Urdu, due to limited annotated da-ta and inherent linguistic complexities. In this paper, we propose a novel Machine Reading Comprehension (MRC)-based approach that effectively addresses the JERE task for Urdu, integrating a text encoder and a question-answering module that work synergistically to enhance entity and relationship extraction. We introduce an annotated Urdu JERE dataset and demonstrate how our methodology will significantly contribute to multilingual NLP efforts. We propose an innovative Machine Reading Comprehension (MRC)-based method to tackle JERE in Urdu. This method has two main components: a text encoder and a question answering (QA) module. The text encoder converts Urdu text into a compact vector form, which is then fed into the QA module. The QA module generates answers to queries regarding the desired entities and relationships, producing a sequence of tokens that represent these entities and their interactions. The model is trained to minimize the difference between its predicted answers and the correct ones. Our approach, along with the introduction of an annotated Urdu JERE dataset, significantly advances multilingual NLP and information ex-traction research. The insights gained can be applied to other low-resource languages, aiding in the development of NLP tools and applications for a broader array of languages.

The role of civil society organizations (CSOs) as community-based knowledge brokers: A qualitative study with CSOs during the first year of the COVID-19 pandemic in Canada

Background: This study explored how civil society organizations in British Columbia, Canada, obtained, shared, and communicated multilingual COVID-19 information with people whose first language is not English. Aim: The aim was to examine civil society organizations’ role as community-based knowledge brokers during the first year of the COVID-19 pandemic. Methods: Commencing in December 2022, virtual semi-structured interviews were conducted with employees from civil society organizations in British Columbia (N=15). Results: Civil society organizations played a crucial role in sharing multilingual information with people whose first language is not English. They amplified public health messages, addressed confusion concerning public health orders, and engaged with community members to better understand and address local needs. Discussion: Civil society organizations contributed to health communication efforts and succeeded in reaching populations overlooked by mainstream communication channels. Conclusions: The COVID-19 pandemic provides an opportunity to reflect on the role of civil society organizations as community-based knowledge brokers that acted as intermediaries to support information-sharing from government public health communications to priority populations. Based on this study’s findings, we propose several recommendations to enhance equity-based preparedness, responses, and re­covery for health emergencies.

Cross-cultural adaptation of the writer-reader relationship in non-translated and translated English health information websites on HIV and TB diagnostic testing

The ideal multilingual health information website is relatable to all readers. Natives and immigrants should have a culturally adapted website in their language hosted by their place of residence that imparts the facts and incites a call to action to improve health, and ultimately reduces disease in the diverse community. The writer’s word choices and attitude as conveyed through the text influence the reader’s decision-making process. This paper will examine the differences between the non-translated and translated English versions of multilingual health information websites on HIV and TB diagnostic testing. These samples pertain to a large interdisciplinary study whose purpose is to determine whether the multilingual health communication websites are appropriately written regarding health literacy, and whether each cultural population, in terms of language adaptation, would receive the health information as intended. The study questions whether there exist differences between the translated and non-translated texts in English, Spanish and Catalan; this paper focuses on the two English sub-corpora. A comparable corpus of seventy-three multilingual health information websites underwent a quantitative and a qualitative analysis. The methodology is based on adaptations of Clerehan et al.’s (2005) Evaluative Linguistic Framework to assess the writer-reader relationship. The findings show statistically significant differences between the two English subcorpora as regards the writers’ and translators’ approach; the non-translated English sub-corpus contained more relational and engagement markers, whereas the translated English sub-corpus had more persuasion markers. These results should serve researchers and professionals in the translation and language sciences as well as the public health field for, respectively, future studies and techniques to improve the composition of multilingual health information texts in culturally diverse countries.

Enhancing multilingual information retrieval: The efficacy of hybrid approaches

Multilingual Information Retrieval (MLIR) plays a crucial role in accessing information across different languages. This paper explores various techniques and tools used in Cross-Language Information Retrieval (CLIR), focusing on query translation, document translation, and hybrid approaches. Query translation employs bilingual dictionaries and machine translation systems to convert user queries from one language to another, whereas document translation involves translating documents into the query language for indexing and retrieval. Hybrid approaches combine these methods to optimize retrieval performance, leveraging the strengths of both to address their individual limitations. Our comparative analysis shows that hybrid systems consistently outperform standalone query or document translation systems, achieving higher precision, recall, and user satisfaction. For instance, hybrid systems in multilingual legal document retrieval tasks achieved precision rates of 88%, recall rates of 82%, and an F1 score of 0.85. These results underscore the effectiveness of hybrid approaches in handling the complexities of MLIR, providing more accurate and comprehensive retrieval outcomes. This study highlights the practical benefits of hybrid CLIR systems and suggests directions for future research in enhancing multilingual access to information.

DFNet: Decoupled Fusion Network for Dialectal Speech Recognition

Deep learning is often inadequate for achieving effective dialect recognition in situations where data are limited and model training is complex. Differences between Mandarin and dialects, such as the varied pronunciation variants and distinct linguistic features of dialects, often result in a significant decline in recognition performance. In addition, existing work often overlooks the similarities between Mandarin and its dialects and fails to leverage these connections to enhance recognition accuracy. To address these challenges, we propose the Decoupled Fusion Network (DFNet). This network extracts acoustic private and shared features of different languages through feature decoupling, which enhances adaptation to the uniqueness and similarity of these two speech patterns. In addition, we designed a heterogeneous information-weighted fusion module to effectively combine the decoupled Mandarin and dialect features. This strategy leverages the similarity between Mandarin and its dialects, enabling the sharing of multilingual information, and notably enhance the model’s recognition capabilities on low-resource dialect data. An evaluation of our method on the Henan and Guangdong datasets shows that the DFNet performance has improved by 2.64% and 2.68%, respectively. Additionally, a significant number of ablation comparison experiments demonstrate the effectiveness of the method.

Design of English Lexical Analysis System for Machine Translation Based on Multimodal Neural Network

Information resources have become a very important source of wealth, and due to differences in language and writing, technological information exchange between countries has become difficult. In this article, we propose a data augmentation method based on statistical models. A framework for a multimodal output model is proposed, taking into account the semantic relevance and importance between different languages. This framework is based on text sequence to sequence framework, decoupled, and a network architecture based on dual stream attention mechanism is designed. A multimodal interactive neural network layer was added between the encoder and decoder, achieving the fusion of multilingual domain information. It compensates for the influence of text on traditional translation generation systems in the process of English Chinese translation, and solves the problems of inaccurate translation results and low similarity with the original text in English Chinese machine translation. The system proposed in this article mainly consists of preprocessing module, lexical analysis and segmentation module, part of speech tagging and phrase analysis module, translation rule construction module, decoding module, translation generation module, etc. The experimental results show that using an improved generation system, compared with traditional generation systems, improves the accuracy of translation generation, has certain advantages, and is more practical.

Examining the Content of Disaster-Related Information Disseminated to a Migrant Population

Many local governments, especially in Japan, disseminate multilingual disaster-related information to migrants as part of their disaster education programs to make their disaster risk reduction (DRR) policies more inclusive. However, little is known about the specific content of the information disseminated through such programs and how or why that content is selected. Using an example case of Okayama Prefecture in Japan and its principal city, Okayama City, the current study identifies the content of multilingual disaster-related information, as well as the assumptions behind its selection. To this end, the content of multilingual disaster-related information disseminated by these local governments was inductively analyzed, focusing on (1) what is and is not communicated, (2) how the government views the migrant population, and (3) their goals of information dissemination targeting migrants. As a result of conducting a qualitative content analysis on information disseminated by the Okayama local governments via information booklets and a YouTube video, it was found that the content was largely concerned with calling migrants to be proactive in taking preventive and harm reduction measures, as well as acquiring prerequisite knowledge (e.g., about disasters and their management). The underlying assumptions and expectations behind this content (e.g., how a behavioral change occurs and self-sufficiency as an expectation in disaster preparedness) are discussed, as well as issues associated with these assumptions.

Advancements in Neural Machine Translation: Methodological Innovations and Empirical Insights for Cross-Linguistic Discourse Preservation

Abstract: Natural Language Processing (NLP), in the form recognizable today, really began to take hold in the 1980s, when machine learning helped propel it to soaring heights. However, due to a lack of processing power, machine learning, and to an extent, NLP, started slowing down in innovation and ideas and had almost ground to a relative halt, until the last decade, when a sudden increase in both productivity and interest in the machine learning helped increase the amount of knowledge in the space itself. This review provides several different case studies using different methodologies. The first paper was a deep analysis on how researchers were able to use Tesseract and Google Vision in tandem with automatic data mining methods to enrich the Cherokee language database in order to preserve it from extinction. The second paper takes a query translation-based approach toward translating English to Indian languages and utilizes a Multilingual Cross-Language Information Retrieval (MLCIR) system with tools such as Part of Speech Tagger (POST), Stop-Word, and Porter Stemmer. The third paper presents CoVe, which transfers knowledge from machine translation to improve performance on NLP tasks like sentiment analysis and question answering by using contextualized word vectors along with word embeddings, achieving new state-of-the-art results on some datasets. The fourth paper aims to translate English to Pakistan Sign Language (PSL) and also uses POST and goes through dependency analysis, sentence classification, and PSL using PLS trees. The fifth paper uses a Multilingual Neural Machine Translation (NMT) system for LowResource languages and incorporates two main models: a recurrent NMT and a transformer NMT. The sixth paper analyzes how a fine-tuned transformer model seems to work better than transformer models trained from scratch on high-resource languages, while vice-versa seems to occur for low-resource languages. The seventh paper adds to this by talking about how multilingual translation seems to work better than a back-translation model. Given the diverse array of approaches that could be used, we aim to identify the most efficient and correct methodology for future researchers to use in their work, based on the papers in this literature review.

Multilingual Information Retrieval Using Graph Neural Networks: Practical Applications in English Translation

Multilingual information retrieval using graph neural networks offers practical applications in English translation by leveraging advanced computational models to enhance the efficiency and accuracy of cross-lingual search and translation tasks. By representing textual data as graphs and utilizing graph neural networks (GNNs), this approach captures intricate relationships between words and phrases across different languages, enabling more effective language understanding and translation. GNNs can learn complex linguistic structures and semantic similarities from multilingual corpora, facilitating the development of more robust translation systems that are capable of handling diverse language pairs and domains. The paper introduces a novel approach termed the Multilingual Ant Bee Optimization Graph Neural Network (MABO-GNN) for addressing optimization, classification, and multilingual translation tasks. MABO-GNN integrates ant bee optimization algorithms with graph neural networks to provide a versatile framework capable of optimizing objective functions, improving classification accuracy iteratively, and facilitating high-quality translations across multiple languages. Through comprehensive experimentation, the efficacy of MABO-GNN is demonstrated across various tasks, languages, and datasets. in optimization experiments, MABO-GNN achieves objective function values of 0.012, 0.015, 0.011, and 0.013 in Experiment 1, Experiment 2, Experiment 3, and Experiment 4, respectively, with convergence times ranging from 90 to 150 seconds. In classification tasks, the model exhibits notable performance improvements over iterations, with BLEU scores reaching 0.84 and METEOR scores reaching 0.78 in the fifth iteration. The translation results showcase BLEU scores of 0.85 for English, 0.82 for French, 0.79 for German, 0.81 for Spanish, and 0.75 for Chinese, indicating the model's proficiency in generating high-quality translations across diverse languages.

The role of languages: adopting the motivation-opportunity-ability (MOA) model to explore information sharing patterns among multilingual users in the Chinese cultural context

PurposeCulture is considered a critical aspect of social media usage. The purpose of this paper is to explore how cultures and languages influence multilingual users' cross-cultural information sharing patterns.Design/methodology/approachThis study used a crowdsourcing survey with Amazon Mechanical Turk to collect qualitative and quantitative data from 355 multilingual users who utilize two or more languages daily. A mixed-method approach combined statistical, and cluster analysis with thematic analysis was employed to analyze information sharing patterns among multilingual users in the Chinese cultural context.FindingsIt was found that most multilingual users surveyed preferred to share in their first and second language mainly because that is what others around them speak or use. Multilingual users have more diverse sharing characteristics and are more actively engaged in social media. The results also provide insights into what incentives make multilingual users engage in social media to share information related to Chinese culture with the MOA model. Finally, the ten motivation factors include learning, entertainment, empathy, personal gain, social engagement, altruism, self-expression, information, trust and sharing culture. One opportunity factor is identified, which is convenience. Three ability factors are recognized consist of self-efficacy, habit and personality.Originality/valueThe findings are conducive to promoting the active participation of multilingual users in online communities, increasing global resource sharing and information flow and promoting the consumption of digital cultural content.

Assessing the impact of an online dementia awareness initiative co-created with and for English, Arabicand Vietnamesespeaking communities: A case study.

Awareness and understanding of dementia remain limited in ethnically diverse populations in multicultural societies due to culturally inappropriate and inaccessible information. To establish the impact, helpersand hinderers of an online multilingual dementia awareness initiative co-created with and for English, Arabicand Vietnamese speaking people. A case study using mixed methods to assess the impact and implementation of an information session on dementia knowledge. The study was conducted with English, Arabicand Vietnamese speaking individuals in Canterbury-Bankstown, Australia. A dementia alliance co-created an online multilingual dementia information session, which was delivered synchronously in English, Arabicand Vietnamese by trained facilitators. In-session group discussions, quizzesand a postsession survey assessed the impact on dementia knowledge. A postimplementation focus group explored the factors that helped and hindered the initiative. The online dementia information session successfully supported participants understanding of dementia causes, impactsand care strategies. The initiative was hindered by competing priorities and limited accessibility to target audiences, while it was helped by the support of an established organisation and feedback mechanisms. Ongoing dementia education and awareness-raising campaigns that are culturally sensitive are needed in communities to promote dementia literacy and help-seeking. An online multilingual dementia information session can be an effective way to improve dementia literacy and advocate for change in multicultural communities. English, Arabicand Vietnamese speaking members of the Canterbury Bankstown Dementia Alliance participated in the co-creation and evaluation of this initiative.

A Privacy-Preserving Multilingual Comparable Corpus Construction Method in Internet of Things

With the expansion of the Internet of Things (IoT) and artificial intelligence (AI) technologies, multilingual scenarios are gradually increasing, and applications based on multilingual resources are also on the rise. In this process, apart from the need for the construction of multilingual resources, privacy protection issues like data privacy leakage are increasingly highlighted. Comparable corpus is important in multilingual language information processing in IoT. However, the multilingual comparable corpus concerning privacy preserving is rare, so there is an urgent need to construct a multilingual corpus resource. This paper proposes a method for constructing a privacy-preserving multilingual comparable corpus, taking Chinese–Uighur–Tibetan IoT based news as an example, and mapping the different language texts to a unified language vector space to avoid sensitive information, then calculates the similarity between different language texts and serves as a comparability index to construct comparable relations. Through the decision-making mechanism of minimizing the impossibility, it can identify a comparable corpus pair of multilingual texts based on chapter size to realize the construction of a privacy-preserving Chinese–Uighur–Tibetan comparable corpus (CUTCC). Evaluation experiments demonstrate the effectiveness of our proposed provable method, which outperforms in accuracy rate by 77%, recall rate by 34% and F value by 47.17%. The CUTCC provides valuable privacy-preserving data resources support and language service for multilingual situations in IoT.

HYBRID DISTANCE-STATISTICAL-BASED PHRASE ALIGNMENT FOR ANALYZING PARALLEL TEXTS IN STANDARD MALAY AND MALAY DIALECTS

Parallel texts corpora are essential resources in linguistics and natural language processing, especially in translation and multilingual information retrieval. The publicly available parallel text corpora are limited to certain genres, types and domains. Furthermore, the parallel dialect text is scarce, even though they are important in the analysis and study of a dialect. Collecting parallel dialect text is challenging because dialects typically appear in the form of speech and very limited dialectic texts exist. Moreover, there is no standard orthography in most dialects. The contributions of this paper are threefold. First, the paper describes a methodology in acquiring a parallel text corpus of Standard Malay and Malay dialects, particularly Kelantan Malay and Sarawak Malay. Second, we propose a hybrid of distance based and statistical-based alignment algorithm to align words and phrases the parallel text. The results show that the precision and recall values of the proposed alignment algorithm are more than 95% and better than the state-of the-art GIZA++. Third, the alignment obtained were compared to find out the lexical similarities and differences between Standard Malay and the two studied Malay dialects, contributing valuable insights into the linguistic variations within the Malay language family.

Development of an error correction algorithm for Kazakh language

This article discusses a method for correcting spelling errors in the Kazakh language using the advantages of morphological analysis and a model based on noisy channels. To achieve this goal, modern problems of automatic processing of Kazakh textual information were analyzed, existing linguistic resources and processing systems of the Kazakh language were systematized, the basic requirements for the development of a system for analyzing Kazakh textual information based on machine learning were determined, and models and algorithms for extracting facts from unstructured and poorly structured text arrays were developed. The search function, an enhanced spelling correction algorithm, was utilized in this work and has the ability to recommend the proper spelling of the input text. The maximum editing distance, whether to include the original word when near matches are not found, and how to handle case sensitivity and exclusion based on regular expressions are all easily adjustable features of this functionality. Because of their adaptability, algorithms can be applied to a wide range of problems, from straightforward spell checks in user interfaces to intricate natural language processing assignments. Because of the way it’s designed, the search function finds possible corrections and verifies the context of words while accounting for user preferences like verbosity and ignore markers. Most modern multilingual natural language processing programs use only the graphical stage of text processing. On the other hand, semantic text analysis or analysis of the meaning of natural language is still an important problem in the theory of artificial intelligence and computational linguistics. But in order to process the grammar and semantics of multilingual information, precreated semantic and grammatical corpora of each natural language are necessary. To solve this problem, several tasks were considered and solved. These tasks included the analysis of research in the field of machine learning methods used in the processing of textual information, the existing problems of formalization and modeling of the Kazakh language, as well as the development and implementation of models, methods and algorithms for morphological and semantic analysis of texts of the Kazakh language.

Attitudes and barriers to participation in window-of-opportunity trials reported by White and Asian/Asian British ethnicity patients who have undergone treatment for endometrial cancer

PurposeWindow-of-opportunity trials (WOT) are a study design that have been used to investigate drug activity in endometrial cancer (EC). Recruitment to cancer clinical trials by patients from ethnic minority groups is reported to be lower than for patients of White ethnicity.MethodsA verbal questionnaire was conducted with White and Asian/Asian British ethnicity patients who had undergone treatment for EC. Strategic purposeful sampling was used to recruit patients from diverse social/educational backgrounds. Questions explored: background knowledge of clinical research, WOT study design, and views on medications that might be investigated. Thematic analysis was used to explore motivations for WOT participation and perceived barriers.ResultsIn total, 21 patients were recruited to the study (15 White and 6 Asian/Asian British). Views on optimum time to receive trial information differed, preferences ranging from 'at the time of diagnosis' to 'a few days after diagnosis'. The choice of medication under investigation had a strong influence on potential willingness to participate, with greater interest reported in medications derived from vitamins or food supplements rather than hormone-based drugs. Potential barriers to participation included concern over potential side-effects and the emotional/physical burden of a cancer diagnosis prior to major surgery.DiscussionThis study provides important insights into patients’ views on WOT participation in EC and raises issues that need to be considered for future trial design and participant recruitment materials. The timing and format of study information and type of substance under investigation were factors influencing potential participation. Future studies should consider using multi-lingual visual information videos to address information needs, as this may encourage participation by ethnic minority patients.

Saavutettavuudesta kriittiseen autonomiaan – vieraskieliset kuntavaaliviestinnän kohderyhmänä

The article examines multilingual communication practices aimed at informing Finland’s foreign language speaking inhabitants on municipal elections and decision-making in the regional context of Joensuu. Our focus is on the availability and accessibility of relevant multilingual information and the potential of accessible information in developing a sense of democratic participation and critical autonomy among the immigrant population. The research is based on seven thematic interviews conducted after the 2021 municipal elections. All interviewees have experience in enabling the democratic participation of multilingual residents in Joensuu through their roles in civil service, politics, or local organizations. Poor accessibility of information has been identified as a reason for multilingual population’s low voting rates in previous research. The interview data reveal in detail what kinds of deficiencies can be identified across the components of usability, understandability, and social accessibility. The results also give insights into good practices and possible remedies.

Multilingual Covid-19 Communication and Linguistic Minorities in Finland: A Qualitative Study

Abstract Background Inequality in access to information based on origin have been demonstrated already prior to Covid-19 epidemic and the epidemic widened those differences. Effective multilingual crisis communication can improve equal access to information. However, studies examining experiences of multilingual crisis communication are scarce. This study aims to explore how Arabic, Somali, and Russian speakers in Finland experienced multilingual communication during the Covid-19 epidemic and identify opportunities to improve its effectiveness. Methods Qualitative data consisted of 6 semi-structured group interviews with 32 individuals (Arabic n = 9, Russian n = 12, Somali n = 11 speakers). The respondents had lived in Finland since at least from spring 2021 and spoke Finnish or Swedish at most at beginner level. Interviews were conducted between December 2022 and April 2023 together with a translator. Interviews were analyzed thematically. Results There were major contradictions in respondents’ opinions. According to some respondents, multilingual information provided by authorities was highly valued in their communities and respondents felt that it strengthened trust towards the authorities. Other felt that messages were irrelevant for the communities, and the messages actually often reduced willingness to follow the guidelines or trust official sources. It was agreed that lack of multilingual information created space for dis- and misinformation. Also, the role of mouth-to-mouth information was seen as crucial during Covid-19 epidemic. Official information often neglected important social and cultural aspects and the information from the authorities only reached a small proportion of linguistic minorities living in Finland. Conclusions Multilingual crisis communication should consider social and cultural aspects to strengthen the equal access to trustworthy information of linguistic minorities. By this way, it also builds the trust towards national authorities. Key messages • Multilingual crisis communications that considers also social and cultural aspects is an effective way to improve equity of linguistic minorities during crises. • Multichannel approach and cooperation with communities during crises helps to reach more people and builds trust towards the authorities.

Cross Language Information Retrieval based on Automatic Query Translation for Marathi Documents

Abstract: The current research article explores the realm of Cross Language Information Retrieval (CLIR) and its significance in the digital age. It addresses the challenges faced in CLIR, including lexical and semantic disparities, the scarcity of parallel corpora, cultural nuances, and more. The article discusses innovative solutions encompassing Machine Translation, Query Expansion, Cross-Lingual Word Embeddings, and Multilingual Information Retrieval Models to enhance CLIR's effectiveness. Furthermore, it sheds light on Information Retrieval Models, such as the Boolean Model, Vector Space Model (VSM), and Probabilistic Models, explaining their principles and applications. The study also presents experimental results highlighting the limitations of monolingual IR models and the effectiveness of crosslingual techniques, such as translation and query expansion, in improving CLIR, making it a valuable tool for accessing information across languages.