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Recently, more data-driven approaches are demanding multilingual parallel resources primarily in the cross-language studies. To meet these demands, building multilingual parallel corpora are becoming the focus of many Natural Language Processing (NLP) scientific groups. Unlike monolingual corpora, the number of available multilingual parallel corpora is limited. In this paper, the MulTed, a corpus of subtitles extracted from TEDx talks is introduced. It is multilingual, Part of Speech (PoS) tagged, and bilingually sentence-aligned with English as a pivot language. This corpus is designed for many NLP applications, where the sentence-alignment, the PoS tagging, and the size of corpora are influential such as statistical machine translation, language recognition, and bilingual dictionary generation. Currently, the corpus has subtitles that cover 1100 talks available in over 100 languages. The subtitles are classified based on a variety of topics such as Business, Education, and Sport. Regarding the PoS tagging, the Treetagger, a language-independent PoS tagger, is used; then, to make the PoS tagging maximally useful, a mapping process to a universal common tagset is performed. Finally, we believe that making the MulTed corpus available for a public use can be a significant contribution to the literature of NLP and corpus linguistics, especially for under-resourced languages.

This study aims to examine the effects of using mobile translation applications for translating collocations.

The study followed an experimental design where 47 students of English as foreign language in a Saudi university were randomly categorized into two groups. Both the groups were given a translation task consisting of 30 sentences with fixed, medium-strength and weak collocations. The participants in the experimental group (n 23) were asked to use a mobile App (Reverso) to translate the sentences, while the control group (n 24) was allowed to use only paper-based dictionaries. The translations were scored and analyzed to measure if there was any significant difference between the two groups.

The results indicated that the mobile translation application was more effective in translating fixed and medium-strength collocations than weak collocations, and in translating collocations in both translation directions (i.e. from Arabic into English or vice-versa).

The findings suggest that integrating translation technologies in general and mobile translation applications in particular in translation can enhance the translation process. Students can utilize mobile translation applications to enhance their translation skills, especially for translating collocations.

This paper purposed a multi-facet sentiment analysis system.

Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.

The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.

The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

The paper examines academic historians' information interactions with material from digital historical-newspaper collections as the research process unfolds.

The study employed qualitative analysis from in-depth interviews with Finnish history scholars who use digitised historical newspapers as primary sources for their research. A model for task-based information interaction guided the collection and analysis of data.

The study revealed numerous information interactions within activities related to task-planning, the search process, selecting and working with the items and synthesis and reporting. The information interactions differ with the activities involved, which call for system support mechanisms specific to each activity type. Various activities feature information search, which is an essential research method for those using digital collections in the compilation and analysis of data. Furthermore, application of quantitative methods and multidisciplinary collaboration may be shaping culture in history research toward convergence with the research culture of the natural sciences.

For sustainable digital humanities infrastructure and digital collections, it is of great importance that system designers understand how the collections are accessed, why and their use in the real-world context. The study enriches understanding of the collections' utilisation and advances a theoretical framework for explicating task-based information interaction.

Effective translation has become essential for seamless cross-cultural communication in an era of global interconnectedness. Translation management systems (TMS) have redefined the translation landscape, revolutionizing project management and execution. This study examines the attitudes of translation agencies and professional translators towards integrating and utilizing TMS, with a specific focus on Saudi Arabia.

The study's design was based on a thorough mixed-methods strategy that purposefully combined quantitative and qualitative procedures to create an array of findings. Through a survey involving 35 participants (both project managers and professional translators) and a series of interviews, this research explores the adoption of TMS, perceived benefits, influencing factors and future considerations. This integrated approach sought to investigate the nuanced perceptions of Saudi translation companies and expert translators about TMS. By combining the strengths of quantitative data's broad scopes and qualitative insights' depth, this mixed-methods approach sought to overcome the limitations of each method, ultimately resulting in a holistic understanding of the multifaceted factors shaping attitudes within Saudi Arabia's unique translation landscape.

Based on questionnaires and interviews, the study shows that 80% of participants were familiar with TMS, and 57% had adopted it in their work. Benefits included enhanced project efficiency, collaboration and quality assurance. Factors influencing adoption encompassed cost, compatibility and resistance to change. The study further delved into participants' demographic profiles and years of experience, with a notable concentration in the 6–10 years range. TMS adoption was linked to improved translation processes, and participants expressed interest in AI integration and mobile compatibility. Deployment models favored cloud-based solutions, and compliance with industry standards was deemed vital. The findings underscore the evolving nature of TMS adoption in Saudi Arabia, with diverse attitudes shaped by cultural influences, technological compatibility and awareness.

This research provides a holistic and profound perspective on the integration of TMS, fostering a more comprehensive understanding of the opportunities, obstacles and potential pathways to success. As the translation landscape continues to evolve, the findings from this study will serve as a valuable compass guiding practitioners and researchers towards effectively harnessing the power of technology for enhanced translation outcomes.

As the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.

This paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.

The performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.

The proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.

This study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.

Predicting the stock market's prices has always been an interesting topic since its closely related to making money. Recently, the advances in natural language processing (NLP) have opened new perspectives for solving this task. The purpose of this paper is to show a state-of-the-art natural language approach to using language in predicting the stock market.

In this paper, the conventional statistical models for time-series prediction are implemented as a benchmark. Then, for methodological comparison, various state-of-the-art natural language models ranging from the baseline convolutional and recurrent neural network models to the most advanced transformer-based models are developed, implemented and tested.

Experimental results show that there is a correlation between the textual information in the news headlines and stock price prediction. The model based on the GRU (gated recurrent unit) cell with one linear layer, which takes pairs of the historical prices and the sentiment score calculated using transformer-based models, achieved the best result.

This study provides an insight into how to use NLP to improve stock price prediction and shows that there is a correlation between news headlines and stock price prediction.

Few studies to date have explored factors contributing to the dining experience from a visitor’s perspective. The purpose of this study is to investigate whether different restaurant attributes are critical in evaluating the restaurant experience in online reviews for visitors (non-local) and local guests.

In all, 100,831 online restaurant reviews retrieved from TripAdvisor are analyzed by using domain-specific aspect-based sentiment detection. The influence of different restaurant features on the overall evaluation of visitors and locals is determined and the most critical factors are identified by the frequency of their online discussion.

There are significant differences between locals and visitors regarding the impact of busyness, payment options, atmosphere and location on the overall star rating. Furthermore, the valence of the factors drinks, facilities, food, busyness and menu found in the reviews also differs significantly between the two types of guests.

The findings of this study help restaurant managers to better understand the different customer needs. Based on the results, they can better decide which restaurant aspects should receive the most attention to ensure that customers are satisfied.

Research on online reviews has largely neglected the role of different visitation motives. This study assumes that the reviews of local and non-local restaurant visitors are based on different factors and separates them to gain a more fine-grained and realistic picture of the relevant factors for each particular group.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

The spread of Covid-19 has led to the closure of educational institutions worldwide, forcing academic institutions to find online platforms. The purpose of this paper is to accelerate the development of the online learning (OL) environments within those institutions. The Covid-19 pandemic has unfolded the extent of the academic institutions' readiness to deal with such a crisis.

In this vein, the study aimed to identify the perception of translation instructors in teaching translation courses online during Covid-19, using a questionnaire to explore the strategies and challenges of teaching and assessing students' performance. The analysis revealed instructors' reliance on Zoom and Microsoft Teams in offering virtual classes and WhatsApp in communication with students outside the class.

The findings revealed the relative effectiveness of online education, but its efficacy is less than face-to-face learning according to the respondents' views. It was also found that students faced difficulties in OL, which lie in adapting to the online environment, lack of interaction and motivation and the deficiency of data connections. Even though online education could work as an aid during Covid-19, but it could not replace face-to-face instruction. Based on the findings, the study recommended blended learning. Combining online education with face-to-face instruction, i.e. face-to-face plus synchronous and asynchronous, would result in a rigorous OL environment.

The research is genuine and there is no conflict of interest.