With the rise of computers in the digital world, natural language processing has emerged to deal with human interactions of natural language, like text and speech. The field of Natural Language Processing (NLP) is a branch of artificial intelligence that solves the practical challenges of understanding human languages.

NLP is also known as computational linguistics, broadly categorized into fundamental and applicative research areas. Major tasks in the branch of fundamental NLP research involve syntactic processing, morphological analysis, semantic analysis, and language modeling.

In the branch of NLP applicative topics, tasks involve extracting potential information such as the relation extraction and named entity extraction, text translation, summarization, question answering, text classification, and text clustering.

In addition to the shallow learning models, deep neural networks have achieved superior outcomes over a variety of natural language-related tasks.

As a result, NLP has utilized the advantages of neural network algorithms to handle the ever-expanding availability of conversational data. In recent years, Google’s search engine and Amazon’s voice assistance are the popular NLP-based systems.

In the real world, NLP research has emerged for various text and voice processing tasks. Several widely popular NLP research areas are presented as follows.

Information Retrieval:
Information retrieval is the process of retrieving the most relevant information for a particular query posed by the user. For example, Google search is one of the popular information retrieval models. To retrieve the most appropriate set of documents, an information retrieval system searches a collection of documents.

Text Summarization:
With the vast amount of textual content availability, understanding the context of the information from lengthy chunks of text becomes hard and leverages high user perplexity. Text summarization resolves this constraint by providing a concise summary representing the information conveyed in a lengthy sequence of words. Financial research, media monitoring, question-answer bots, and social media marketing domains have greatly benefited text summarization.

Information Extraction:
Information extraction is the task of parsing the significant information from the unstructured vast amount of text data, averts the hectic and time-consuming process. Many organizations and companies heavily rely on the information extraction models to reduce the expenses and alleviate human effort with the automation in the knowledge discovery and content management.

Text Generation:
In NLP, the text generation kind of natural language generation has become one of the significant and challenging tasks. Text generation automatically generates the natural language texts and satisfies the communicative needs by leveraging artificial intelligence and computational linguistics knowledge. Machine translation and summarization are a wide range of applications of text generation.

Machine Translation:
Machine translation automatically translates the text from one natural language to another without human contribution. Artificial intelligence-based machine translation paves the way to very significant improvements in the quality of text translation.

Question Answering:
Question answering is a critical natural language problem in a sub-field of the information retrieval task. The question answering system enables users to submit their questions in natural language and efficiently provide the most appropriate response. Search engines and telephonic conversational interfaces are integrated with the question answering systems.

Part-of-Speech Tagging:
Part-of-Speech (PoS) tagging is the process of categorizing the textual words in a sentence by labeling the words based on the part of speech categories. Named Entity Recognition (NER) task relies on the part of speech tagging.

Speech Recognition:
Speech recognition enables computer software to convert the natural language speech into a text format by identifying and interpreting the words and phrases in spoken language. Speech recognition is also called computer speech recognition, speech-to-text conversion, and automatic speech recognition, not confused with voice recognition. Nowadays, speech recognition applications involve text message composing or music playing with virtual assistants, which eases the communication gap between humans and computers.

Text Classification:
Text classification, particularly sentiment classification, is one of the NLP tasks. Natural language processing greatly assists the automatic classification of text into pre-defined categories. Sentiment classification automatically analyzes natural language texts and identifies the opinions expressed in the text, resulting in the text with positive, negative, or neutral labels.

NLP encounters a few endless challenges while working on its different tasks, discussed as follows.

Ambiguity:
Natural language often comprises ambiguity in the textual sentences and occurs at word, sentence, or meaning levels. In NLP, understanding the meaning of a word is one of the biggest challenges due to the context of the words changes over time and changes in different domains. Lexical, syntactic, and semantic ambiguity are the types of ambiguities in the natural language text.

Slang:
In the natural language understanding, fluency or pattern of the expressed natural language text varies among the people by their culture, geolocation, age category, educational qualification, and so on. It is hard to build a natural language system that deals with various natural language inputs generated from different categories of people.

Misspelling and Mispronunciation:
Natural language text often comprises misspelled words or short texts, creating significant challenges to the text analysis. Understanding or recognizing the intention of the writer from their misspelled text is arduous for the models or machines. Hence, individual users customize their smart assistants due to the rise of various mispronunciations and accents.

Irony and Sarcasm:
In NLP, understanding the irony and sarcasm of texts is quite challenging due to the opposite meaning of the text with literal meaning. Machine learning models identify irony and sarcasm statements and the positive or negative terms recognized due to the machine learning models analyzing the words and phrases by their definition only. Nowadays, the users increasingly generate the figurative language of irony and sarcasm texts, conveying salient meaning expressed humorously or tragically.

Training Data:
For the machine learning-based natural language processing, modeling or providing the availability of training data to the decision-making system is challenging due to the availability of irrelevant or diversified textual content across various applications. The NLP system relies on the provided training data; hence, modeling the training data without the irrelevant or questionable data is critical even the AI models spend an enormous amount of time on recognizing the language patterns of a particular human.

Attention Mechanism for Natural Language Processing with Deep Learning
• To build the deep neural network-based natural language processing system with the modeling of attention mechanism.
• To design the attention mechanism in deep learning to recognize the natural language text contextually.

Natural Language Processing using Deep Learning
• To develop the deep learning-based decision-making model to perform the natural language task for the immense unstructured text.
• To address the accurate recognition of the massive colloquial text to ensure the adaptive natural language understanding system.

Deep Autoencoder based Text Generation for Natural Language
• To extract the potential features and represent the natural language text to provide the salient training knowledge to the learning model.
• To generate the text sequence for the natural language text from modeling the deep autoencoder-based representation.

Deep Learning-based Contextual Text Generation for Conversational Text
• To generate the text sequence for the conversational application with the analysis of the personalized interactions of the natural language text.
• To investigate the context of the conversation with the help of the deep learning model to build the automated conversation.

Deep Learning-based Contextual Word Embedding for Text Generation
• To represent the natural language text as the word embedding without compromising the context of the sentence.
• To enhance the deep learning-based text generation from the learning of the contextually embedded text.

Discourse Representation-Aware Text Generation using Deep Learning Model
• To build the deep learning-based text generation to leverage the continual automated interaction for the humans’ natural language text.
• To generate the text sequences with the discourse representation across the sentences through the analysis of the discourse relation of the input text.

Pre-trained Deep Learning Model based Text Generation
• To train the learning model on the external knowledge to accurately generate the text even when there is a lack of samples in the training data.
• To develop the pre-trained learning model to provide the adequate knowledge for the sequence generation.

Text Sequence Generation with Deep Transfer Learning
• To adopt the transfer learning model for the text generation with the training of the deep learning model on the relevant source domain.
• To utilize the source domain knowledge on the target domain to generate the text sequence with the contextual terms.

Negation Handling with Contextual Representation for Deep Sentiment Classification
• To develop the sentiment classification model using deep learning with the consideration of the negation terms in the natural language text.
• To represent the natural language text in a contextual vector form and retain the negation term with contextual weight to recognize the sentiment of the text.

Sentiment Classification in Social Media with Deep Contextual Embedding
• To build the sentiment classification model for the social media text using the deep learning.
• To transform the natural language text into the contextual embedding format to handle the complexities hidden in the social media text during the sentiment classification.

Deep Learning-based Emotion Classification in Conversational Text
• To classify the inherent emotions of the expressed natural language text using the deep learning model.
• To extract the emotion-related features from the conversational text to enforce the different types of emotion recognition.

Context-aware Argument Mining with Deep Semi-supervised Learning
• To design the semi-supervised learning model with the deep learning algorithm to determine the argument from the natural language text.
• To contextually extract the inferences of the text towards argument extraction to understand the natural language text.

Deep Bi-directional Text Analysis for Sarcasm Detection
• To detect the figurative language of the sarcasm to extract the opinion of the natural language text accurately.
• To perform the deep learning-based bi-directional text analysis to recognize the salient meaning behind the sarcasm text.

Deep Attentive Model based Irony Text and Sarcasm Detection
• To design the deep learning model to detect the Irony as well as the Sarcasm text to enhance the emotion recognition.
• To combine the attention model with the deep neural network to extract the pattern of the natural language text with the vector of weights.

Emotion Transition Recognition with Contextual Embedding in Sarcasm Detection
• To develop the emotion transition model to detect the sarcasm representation of the natural language text.
• To model the contextual embedding to represent the users’ expressed text to recognize the emotion dynamics within a sentence.

Modeling Deep Semi-Supervised Learning for Non-Redundant Text Generation.
• To design the semi-supervised learning model to ensure the generation of text sequence without redundant terms.
• To adopt the knowledge extracted from the deep unsupervised learning model for the deep supervised learning model to generate the non-redundant sequence.