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Sentiment Analysis (SA) is one of the most active research areas in the Natural Language Processing (NLP) field due to its potential for business and society. With the development of language representation models, numerous methods have shown promising ...

It is essential for the research communities to investigate ways for authenticating news. The use of linguistic feature-based analysis to automatically detect false news is gaining popularity among the scientific community. However, such techniques are ...

Sentence ordering aims at restoring orders of shuffled sentences in a paragraph. Previous methods usually predict orders in a single direction, i.e., from head to tail. However, unidirectional prediction inevitably causes error accumulation, which ...

In recent years, the use of distributed representations has been a fundamental technology for natural language processing. However, Japanese has multiple compound words, and often we must compare the meanings of a word and a compound word. Moreover, word ...

Emotion detection is a widely studied topic in natural language processing due to its significance in a number of application areas. A plethora of studies have been conducted on emotion detection in European as well as Asian languages. However, a large ...

Reasoning over knowledge graphs (KGs) has received increasing attention recently due to its promising applications in many areas, such as semantic search and recommendation systems. Subsequently, most reasoning models are inherently transductive and ...

As a common language form in oral communication, short text is hard to be used in the applications such as intent understanding, text classification and so on due to its limited content and information, as well as irregular expression and missing ...

With the advances in Natural Language Processing (NLP), the industry has been moving towards human-directed artificial intelligence (AI) solutions. Recently, chatbots and automated news generation have captured a lot of attention. The goal is to ...

Learning response generation models constitute the main component of building open-domain dialogue systems. However, training open-domain response generation models requires large amounts of labeled data and pre-trained language generation models that are ...

Recently, the Text-to-SQL task has received much attention. Many sophisticated neural models have been invented that achieve significant results. Most current work assumes that all the inputs are legal and the model should generate an SQL query for any ...

Neural architecture search (NAS) has shown the strong performance of learning neural models automatically in recent years. But most NAS systems are unreliable due to the architecture gap brought by discrete representations of atomic architectures. In this ...

Tokenization is an important text preprocessing step to prepare input tokens for deep language models. WordPiece and BPE are de facto methods employed by important models, such as BERT and GPT. However, the impact of tokenization can be different for ...

The Devanagari script is one of the most widely used scripts worldwide. The existing deep learning-based optical character recognition system for printed Devanagari scripts using Convolutional Neural Network – Recurrent Neural Network, or CRNN is not ...

Multi-view fusion approaches have gained increasing interest in the past few years by researchers. This interest comes due to the many perspectives that datasets can be looked at and evaluated. One of the most urging areas that require constant leveraging ...

Document-level relation extraction (DocRE) aims to extract relations among entities across multiple sentences within a document by using reasoning skills (i.e., pattern recognition, logical reasoning, coreference reasoning, etc.) related to the reasoning ...

Text readability assessment is a well-known problem that has acquired even more importance in today’s information-rich world. In this article, we survey various approaches to measuring and assessing the readability of texts. Our specific goal is to ...

This article tackles the problem of sentiment analysis in the Arabic language where a new deep learning model has been put forward. The proposed model uses a hybrid bidirectional gated recurrent unit (BiGRU) and bidirectional long short-term memory (...

The explosive growth of social media has fueled an extensive increase in online freedom of speech. The worldwide platform of human voice creates possibilities to assail other users without facing any consequences, and flout social etiquettes, resulting in ...

Benefiting from the improvement of positional encoding and the introduction of lexical knowledge, Transformer has achieved superior performance than the prevailing BiLSTM-based models in named entity recognition (NER) task. However, existing Transformer-...

Studies on natural language processing are mainly conducted in English, with very few exploring languages that are under-resourced, including the Dravidian languages. We present a novel work in detecting offensive language using a corpus collected from ...