The number of sentence pairs in the bilingual corpus is a key to translation accuracy in computational machine translations. However, if the amount goes beyond a certain degree, the increasing number of cases has less impact on the translation while the construction of translation systems requires a considerable amount of time and energy, thus preventing the development of a statistical translation

The core of micro-business and consumer transactions is trust. Based on the Theory of Reasoned Action and Technology Acceptance Model, this article discusses the factors of the establishment and promotion of micro-business trust from the trust orientation of consumer, the trust of WeChat businesses, and the trust of WeChat platform. Data were obtained by questionnaire, and SPSS software was used for

As the number of digital text documents increases on a daily basis, the classification of text is becoming a challenging task. Each text document consists of a large number of words (or features) that drive down the efficiency of a classification algorithm. This article presents an optimized feature selection algorithm designed to reduce a large number of features to improve the accuracy of the text

The quick growth and fast spread of electronic word-of-mouth (eWOM) have created a new threat to Internet merchants and marketers through paid online reviewers flooding sites with product and service reviews that could confuse and deter customers. This study examined the effects of the posts by paid reviewers—specifically, the negative reviews—on consumers’ risk perception, product attitude, and purchase

Topic modeling is an unsupervised learning task that discovers the hidden topics in a collection of documents. In turn, the discovered topics can be used for summarizing, organizing, and understanding the documents in the collection. Most of the existing techniques for topic modeling are derivatives of the Latent Dirichlet Allocation which uses a bag-of-word assumption for the documents. However, bag-of-words

While the amount of digitally available data on the worlds’ languages is steadily increasing, with more and more languages being documented, only a small proportion of the language resources produced are sustainable. Data reuse is often difficult due to idiosyncratic formats and a negligence of standards that could help to increase the comparability of linguistic data. The sustainability problem is

Digital text is quickly becoming essential to modern daily life. The article you are reading right now is born digital; unlike texts of the not-so-distant past, it may never be printed at all. Worldwide, the trend is clear: Digital text is on the way in, and print is on its way out. Year-by-year, more and more readers are turning to ebooks, internet news, and other forms of ereading, while generation

Background: A hadith refers to sayings, actions, and characteristics of the Prophet Muhammad peace be upon him. The authenticity of hadiths is crucial, because they constitute the source of legislation for Muslims with the Holy Quran. Classifying hadiths into groups is a matter of importance as well, to make them easy to search and recognize. Objective: To report the results of a systematic review

Electronic medical records (EMRs) contain valuable information about the patients, such as clinical symptoms, diagnostic results, and medications. Named entity recognition (NER) aims to recognize entities from unstructured text, which is the initial step toward the semantic understanding of the EMRs. Extracting medical information from Chinese EMRs could be a more complicated task because of the difference

Part-of-speech (POS) tagging is a fundamental task in natural language processing. Korean POS tagging consists of two subtasks: morphological analysis and POS tagging. In recent years, scholars have tended to use the seq2seq model to solve this problem. The full context of a sentence is considered in these seq2seq-based Korean POS tagging methods. However, Korean morphological analysis relies more

Over the past decade, through a mixture of optical character recognition and manual input, there is now a growing corpus of Tibetan literature available as e-texts in Unicode format. With the creation of such a corpus, the techniques of text analytics that have been applied in the analysis of English and other modern languages may now be applied to Tibetan. In this work, we narrow our focus to examine

Pāṇini’s grammar is an important milestone in the Indian grammatical tradition. Unlike grammars of other languages, it is almost exhaustive and together with the theories of śābdabodha (verbal cognition), this grammar provides a system for language analysis as well as generation. The theories of śābdabodha describe three conditions necessary for verbal cognition. They are ākāṅkṣā (expectancy), yogyatā

Logographic and alphabetic languages (e.g., Chinese vs. English) have different writing systems linguistically. Languages belonging to the same writing system usually exhibit more sharing information, which can be used to facilitate natural language processing tasks such as neural machine translation (NMT). This article takes advantage of the logographic characters in Chinese and Japanese by decomposing

Diacritics (short vowels) are typically omitted when writing Arabic text, and readers have to reintroduce them to correctly pronounce words. There are two types of Arabic diacritics: The first are core-word diacritics (CW), which specify the lexical selection, and the second are case endings (CE), which typically appear at the end of word stems and generally specify their syntactic roles. Recovering

No abstract available.

Unsupervised neural machine translation (UNMT) has achieved remarkable results for several language pairs, such as French–English and German–English. Most previous studies have focused on modeling UNMT systems; few studies have investigated the effect of UNMT on specific languages. In this article, we first empirically investigate UNMT for four diverse language pairs (French/German/Chinese/Japanese–English)

Disability is an impairment affecting an individual's livelihood and independence. Assistive technology enables the disabled cohort of the community to break the barriers to learning, access information, contribute to the community, and live independently. This article proposes an assistive device to enable people with visual disabilities and learning disabilities to access printed Arabic material

Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic

Frequently corresponding to syntactic components, the Maximal-length Noun Phrase (MNP) possesses abundant syntactic and semantic information and acts a certain semantic role in sentences. Recognition of MNP plays an important role in Natural Language Processing and lays the foundation for analyzing and understanding sentence structure and semantics. By comparing the essence of different MNPs, this

Dependency parsing is an important task for Natural Language Processing (NLP). However, a mature parser requires a large treebank for training, which is still extremely costly to create. Tibetan is a kind of extremely low-resource language for NLP, there is no available Tibetan dependency treebank, which is currently obtained by manual annotation. Furthermore, there are few related kinds of research

Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using

Emotions detection in natural languages is very effective in analyzing the user's mood about a concerned product, news, topic, and so on. However, it is really a challenging task to extract important features from a burst of raw social text, as emotions are subjective with limited fuzzy boundaries. These subjective features can be conveyed in various perceptions and terminologies. In this article,

Subword segmentation plays an important role in Tibetan neural machine translation (NMT). The structure of Tibetan words consists of two levels. First, words consist of a sequence of syllables, and then a syllable consists of a sequence of characters. According to this special word structure, we propose two methods for Tibetan subword segmentation, namely syllable-based and character-based methods

Creating bilingual dictionary is the first crucial step in enriching low-resource languages. Especially for the closely related ones, it has been shown that the constraint-based approach is useful for inducing bilingual lexicons from two bilingual dictionaries via the pivot language. However, if there are no available machine-readable dictionaries as input, we need to consider manual creation by bilingual

The research of Tibetan dependency analysis is mainly limited to two challenges: lack of a dataset and reliance on expert knowledge. To resolve the preceding challenges, we first introduce a new Tibetan dependency analysis dataset, and then propose a neural-based framework that resolves the reliance on the expert knowledge issue by automatically extracting feature vectors of words and predicts their

Image captioning is the process of generating a textual description of an image that aims to describe the salient parts of the given image. It is an important problem, as it involves computer vision and natural language processing, where computer vision is used for understanding images, and natural language processing is used for language modeling. A lot of works have been done for image captioning

This article presents a pipeline that converts collections of Tibetan documents in plain text or XML into a fully segmented and POS-tagged corpus. We apply the pipeline to the large extent collection of the Buddhist Digital Resource Center. The semi-supervised methods presented here not only result in a new and improved version of the largest annotated Tibetan corpus to date, the integration of rule-based

A topic model is one of the best stochastic models for summarizing an extensive collection of text. It has accomplished an inordinate achievement in text analysis as well as text summarization. It can be employed to the set of documents that are represented as a bag-of-words, without considering grammar and order of the words. We modeled the topics for Gujarati news articles corpus. As the Gujarati

Multilingual and multimodal data analysis is the emerging news feed evaluation system. News feed analysis and evaluations are interrelated processes, which are useful in understanding the news factors. The news feed evaluation system can be implemented for single or multilingual language models. Classification techniques used on multilingual news analysis require deep layered learning techniques rather

Hate speech is a specific type of controversial content that is widely legislated as a crime that must be identified and blocked. However, due to the sheer volume and velocity of the Twitter data stream, hate speech detection cannot be performed manually. To address this issue, several studies have been conducted for hate speech detection in European languages, whereas little attention has been paid

Sentence similarity analysis has been applied in many fields, such as machine translation, the question answering system, and voice customer service. As a basic task of natural language processing, sentence similarity analysis plays an important role in many fields. The task of sentence similarity analysis is to establish a sentence similarity scoring model through multi-features. In previous work

The use of offensive language in user-generated content is a serious problem that needs to be addressed with the latest technology. The field of Natural Language Processing (NLP) can support the automatic detection of offensive language. In this survey, we review previous NLP studies that cover Arabic offensive language detection. This survey investigates the state-of-the-art in offensive language

Recent advances, such as GPT, BERT, and RoBERTa, have shown success in incorporating a pre-trained transformer language model and fine-tuning operations to improve downstream NLP systems. However, this framework still has some fundamental problems in effectively incorporating supervised knowledge from other related tasks. In this study, we investigate a transferable BERT (TransBERT) training framework

Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. The availability of a comprehensive benchmarking database for ArSL is one of the

A novel Indonesian sentiment lexicon (SentIL -- Sentiment Indonesian Lexicon) is created with an automatic pipeline; from creating sentiment seed words, adding new words with slang words, emoticons, and from the given dictionary and sentiment corpus, until tuning sentiment value with tagged sentiment corpus. It begins by taking seed words from WordNet Bahasa that mapped with sentiment value from English

The goal of aspect-level sentiment analysis is to identify the sentiment polarity of a specific opinion target expressed; it is a fine-grained sentiment analysis task. Most of the existing works study how to better use the target information to model the sentence without using the interactive information between the sentence and target. In this article, we argue that the prediction of aspect-level

Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author’s local network or author-venue

With recent developments in online social networks (OSNs), these services are widely applied in daily lives. On the other hand, cyberbullying, which is a relatively new type of harassment through the internet-based electronic devices, is rising in online social networks. Accordingly, scholars are attracted to investigating cyberbullying behaviors. Studies show that cyberbullying has a devastating effect

Happiness is a hot topic in academic circles. The study of happiness involves many disciplines, such as philosophy, psychology, sociology, and economics. However, there are few studies on the quantitative analysis of the factors affecting happiness. In this article, we used the well-known World Values Survey Wave 6 (WV6) dataset to quantitatively analyze the happiness of 57 countries with Big Data

The explosion of news text and the development of artificial intelligence provide a new opportunity and challenge to provide high-quality media monitoring service. In this article, we propose a semantic analysis approach based on the Latent Dirichlet Allocation (LDA) and Apriori algorithm, and we realize application to improve media monitoring reports by mining large-scale news text. First, we propose

Named entity recognition (NER) is an important task in natural language understanding, as it extracts the key entities (person, organization, location, date, number, etc.) and objects (product, song, movie, activity name, etc.) mentioned in texts. However, existing natural language processing (NLP) tools (such as Stanford NER) recognize only general named entities or require annotated training examples

In this article, first a comprehensive study of the impact of term weighting schemes on the topic modeling performance (i.e., LDA and DMM) on Arabic long and short texts is presented. We investigate six term weighting methods including Word count method (standard topic models), TFIDF, PMI, BDC, CLPB, and CEW. Moreover, we propose a novel combination term weighting scheme, namely, CmTLB. We utilize

National Happiness Index (NHI) is a national indicator of development that estimates the economic and social well-being of the nation's individuals. With the proliferation of the internet, people share a significant amount of data on social media websites. We can process the data with different sentiment analysis techniques to calculate the NHI. In the literature, different approaches have been used

How can we determine the semantic meaning of a word in relation to its context of appearance? We eventually have to grabble with this difficult question, as one of the paramount problems of Natural Language Processing (NLP). In other words, this issue is commonly defined as Word Sense Disambiguation (WSD). The latter is one of the crucial difficulties within the NLP field. In this respect, word vectors

In this article, conditional-transforming variational autoencoders (CTVAEs) are proposed for generating diverse short text conversations. In conditional variational autoencoders (CVAEs), the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tended to become condition-independent

Success of Natural Language Processing (NLP) models, just like all advanced machine learning models, rely heavily on large -scale lexical resources. For English, English WordNet (EWN) is a leading example of a large-scale resource that has enabled advances in Natural Language Understanding (NLU) tasks such as word sense disambiguation, question answering, sentiment analysis, and emotion recognition

This work presents an in-depth analysis of machine translations of morphologically-rich Indo-Aryan and Dravidian languages under zero-resource conditions. It focuses on Zero-Shot Systems for these languages and leverages transfer-learning by exploiting target-side monolingual corpora and parallel translations from other languages. These systems are compared with direct translations using the BLEU and

Worldwide, literacy is on the rise. This historically unprecedented surge—especially over the past 200 years—has changed nearly everything about the ancient technology of reading. Who reads is changing: Literacy is no longer just for elite, professional readers, but for anyone and everyone. What and why we read is changing: We do not just read difficult texts for academic, religious, legal, or record-keeping

Text summarization is one of the significant tasks of natural language processing, which automatically converts text into a summary. Some summarization systems, for short/long English, and short Chinese text, benefit from advances in the neural encoder-decoder model because of the availability of large datasets. However, the long Chinese text summarization research has been limited to datasets of a

The absence of publicly available reusable test collections for Arabic question answering on the Holy Qur’an has impeded the possibility of fairly comparing the performance of systems in that domain. In this article, we introduce AyaTEC, a reusable test collection for verse-based question answering on the Holy Qur’an, which serves as a common experimental testbed for this task. AyaTEC includes 207

The core aspiration of this proposed work is to classify Tamil characters inscribed in the palm leaf manuscript using an Artificial Neural Network. Tamil palm leaf manuscript characters in the form of images were processed and segmented using contour-based convex hull bounding box segmentation. The segmented characters were transformed into two forms: Binary Coded Value and the Gray-Level Co-occurrence

Relation classification is one of the most fundamental upstream tasks in natural language processing and information extraction. State-of-the-art approaches make use of various deep neural networks (DNNs) to extract higher-level features directly. They can easily access to accurate classification results by taking advantage of both local entity features and global sentential features. Recent works

Chatbots such as Xiaoice have gained huge popularity in recent years. Users frequently mention their favorite works such as songs and movies in conversations with chatbots. Detecting these entities can help design better chat strategies and improve user experience. Existing named entity recognition methods are mainly designed for formal texts, and their performance on the informal chatbot conversation

The methods based on the combination of word-level and character-level features can effectively boost performance on Chinese short text classification. A lot of works concatenate two-level features with little processing, which leads to losing feature information. In this work, we propose a novel framework called Mutual-Attention Convolutional Neural Networks, which integrates word and character-level

Keywords are considered to be important words in the text and can provide a concise representation of the text. With the surge of unlabeled short text on the Internet, automatic keyword extraction task has proven useful in other information processing applications. Graph-based approaches are prevalent unsupervised models for this task. However, most of these methods emphasize the importance of the

Deep neural networks (DNNs) have provably enhanced the state-of-the-art natural language process (NLP) with their capability of feature learning and representation. As one of the more challenging NLP tasks, neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT). However, SMT is usually better

Text Categorization is an important task in the area of Natural Language Processing (NLP). Its goal is to learn a model that can accurately classify any textual document for a given language into one of a set of predefined categories. In the context of the Arabic language, several approaches have been proposed to tackle this problem, many of which are based on the bag-of-words assumption. Even though

Crosslingual word embeddings developed from multiple parallel corpora help in understanding the relationships between languages and improving the prediction quality of machine translation. However, in low resource languages with complex and agglutinative morphologies, inducing good-quality crosslingual embeddings becomes challenging due to the problem of complex morphological forms and rare words.

Query auto-completion (QAC) is a featured function that has been widely adopted by many sub-domains of search. It can dramatically reduce the number of typed characters and avoid spelling mistakes. These merits of QAC are highlighted to improve user satisfaction, especially when users intend to type in a query on mobile devices. In this article, we will present our industrial solution to the personalized

Research on tools for automating the proofreading of Arabic text has received much attention in recent years. There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Our review of previous studies indicates that few Arabic spell-checking research efforts appropriately address