Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends

They have categorized sentences into 6 groups based on emotions and used TLBO technique to help the users in prioritizing their messages based on the emotions attached with the message. Seal et al. (2020) [120] proposed an efficient emotion detection method by searching emotional words from a pre-defined emotional keyword database and analyzing the emotion words, phrasal verbs, and negation words. It is often possible to perform end-to-end training in deep learning for an application.

It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Real-world Examples of NLP in Action

Another great application of NLP is speech recognition, which allows machines to translate spoken language into written text. Again, voice assistants like Siri or Google Assistant are the most illuminating examples in this case. Natural language processing is only gaining momentum across industries, and new applications appear every year. Below we review the most common use cases of NLP to discover more about your business potential of transforming communication with NLP. It has been observed recently that deep learning can enhance the performances in the first four tasks and becomes the state-of-the-art technology for the tasks (e.g. [1–8]).

But ongoing research and development in NLP promise an even brighter future marked by more advancements and trends. This has the potential to make communication more seamless and inclusive than ever before. The infographic below covers the evolution of NLP over time until it reached today’s potential.

2 Challenges

Neural machine translation, i.e. machine translation using deep learning, has significantly outperformed traditional statistical machine translation. The state-of-the art neural translation systems employ sequence-to-sequence learning models comprising RNNs [4–6]. In our view, there are five major tasks in natural language processing, namely classification, matching, translation, structured prediction and the sequential decision process.

• Sharma (2016) [124] analyzed the conversations in Hinglish means mix of English and Hindi languages and identified the usage patterns of PoS.
• It also covers NLP’s objectives, challenges, and latest research developments.
• Currently, deep learning methods have not yet made effective use of the knowledge.
• In case of machine translation, encoder-decoder architecture is used where dimensionality of input and output vector is not known.
• This volume will be of interest to researchers of computational linguistics in academic and non-academic settings and to graduate students in computational linguistics, artificial intelligence and linguistics.
• Xie et al. [154] proposed a neural architecture where candidate answers and their representation learning are constituent centric, guided by a parse tree.

Naive Bayes is a probabilistic algorithm which is based on probability theory and Bayes’ Theorem to predict the tag of a text such as news or customer review. It helps to calculate the probability of each tag for the given text and return the tag with the highest probability. Bayes’ Theorem is used to predict the probability of a feature based on prior knowledge of conditions that might be related to that feature. The choice of area in NLP using Naïve Bayes Classifiers could be in usual tasks such as segmentation and translation but it is also explored in unusual areas like segmentation for infant learning and identifying documents for opinions and facts. Anggraeni et al. (2019) [61] used ML and AI to create a question-and-answer system for retrieving information about hearing loss. They developed I-Chat Bot which understands the user input and provides an appropriate response and produces a model which can be used in the search for information about required hearing impairments.

A Call for A Multidisciplinary Approach

This model is called multi-nominal model, in addition to the Multi-variate Bernoulli model, it also captures information on how many times a word is used in a document. In this paper, we provide a short overview of NLP, then we dive into the different challenges that are facing it, finally, we conclude by presenting recent trends and future research directions that are speculated by the research community. Artificial intelligence has become part of our everyday lives – Alexa and Siri, text and email autocorrect, customer service chatbots. They all use machine learning algorithms and Natural Language Processing (NLP) to process, “understand”, and respond to human language, both written and spoken. Hidden Markov Models are extensively used for speech recognition, where the output sequence is matched to the sequence of individual phonemes. HMM is not restricted to this application; it has several others such as bioinformatics problems, for example, multiple sequence alignment [128].

The use of the BERT model in the legal domain was explored by Chalkidis et al. [20]. There are complex tasks in natural language processing, which may not be easily realized with deep learning alone. It involves language understanding, language generation, dialogue management, knowledge base natural language processing challenges access and inference. Dialogue management can be formalized as a sequential decision process and reinforcement learning can play a critical role. Obviously, combination of deep learning and reinforcement learning could be potentially useful for the task, which is beyond deep learning itself.

Challenges in Natural Language Processing

Information extraction is concerned with identifying phrases of interest of textual data. For many applications, extracting entities such as names, places, events, dates, times, and prices is a powerful way of summarizing the information relevant to a user’s needs. In the case of a domain specific search engine, the automatic identification of important information can increase accuracy and efficiency of a directed search. There is use of hidden Markov models (HMMs) to extract the relevant fields of research papers. These extracted text segments are used to allow searched over specific fields and to provide effective presentation of search results and to match references to papers.

” is interpreted to “Asking for the current time” in semantic analysis whereas in pragmatic analysis, the same sentence may refer to “expressing resentment to someone who missed the due time” in pragmatic analysis. Thus, semantic analysis is the study of the relationship between various linguistic utterances and their meanings, but pragmatic analysis is the study of context which influences our understanding of linguistic expressions. Pragmatic analysis helps users to uncover the intended meaning of the text by applying contextual background knowledge. Rationalist approach or symbolic approach assumes that a crucial part of the knowledge in the human mind is not derived by the senses but is firm in advance, probably by genetic inheritance. It was believed that machines can be made to function like the human brain by giving some fundamental knowledge and reasoning mechanism linguistics knowledge is directly encoded in rule or other forms of representation.

General tendencies and (possible) future directions

The goal of NLP is to accommodate one or more specialties of an algorithm or system. The metric of NLP assess on an algorithmic system allows for the integration of language understanding and language generation. Rospocher et al. [112] purposed a novel modular system for cross-lingual event extraction for English, Dutch, and Italian Texts by using different pipelines for different languages. The pipeline integrates modules for basic NLP processing as well as more advanced tasks such as cross-lingual named entity linking, semantic role labeling and time normalization. Thus, the cross-lingual framework allows for the interpretation of events, participants, locations, and time, as well as the relations between them. Output of these individual pipelines is intended to be used as input for a system that obtains event centric knowledge graphs.

Sentiment analysis, as one of the NLP techniques, works best to analyze customer reviews and social media sentiments to get public opinions about the product or service or follow trends. Until recently, the conventional wisdom was that while AI was better than humans at data-driven decision making tasks, it was still inferior to humans for cognitive and creative ones. But in the past two years language-based AI has advanced by leaps and bounds, changing common notions of what this technology can do. Integrating Natural Language Processing into existing IT infrastructure is a strategic process that requires careful planning and execution. This integration can significantly enhance the capability of businesses to process and understand large volumes of language data, leading to improved decision-making, customer experiences, and operational efficiencies. Event discovery in social media feeds (Benson et al.,2011) [13], using a graphical model to analyze any social media feeds to determine whether it contains the name of a person or name of a venue, place, time etc.

Meet RAGatouille: A Machine Learning Library to Train and Use SOTA Retrieval Model, ColBERT,…

Editors select a small number of articles recently published in the journal that they believe will be particularly

interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the

most exciting work published in the various research areas of the journal. Xie et al. [154] proposed a neural architecture where candidate answers and their representation learning are constituent centric, guided by a parse tree. Under this architecture, the search space of candidate answers is reduced while preserving the hierarchical, syntactic, and compositional structure among constituents.

Modern NLP involves machines’ interaction with human languages for the study of patterns and obtaining meaningful insights. The first objective gives insights of the various important terminologies of NLP and NLG, and can be useful for the readers interested to start their early career in NLP and work relevant to its applications. The second objective of this paper focuses on the history, applications, and recent developments in the field of NLP. The third objective is to discuss datasets, approaches and evaluation metrics used in NLP. The relevant work done in the existing literature with their findings and some of the important applications and projects in NLP are also discussed in the paper.

We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. Emotion detection investigates and identifies the types of emotion from speech, facial expressions, gestures, and text. Sharma (2016) [124] analyzed the conversations in Hinglish means mix of English and Hindi languages and identified the usage patterns of PoS. Their work was based on identification of language and POS tagging of mixed script. They tried to detect emotions in mixed script by relating machine learning and human knowledge.

With deep learning, the representations of data in different forms, such as text and image, can all be learned as real-valued vectors. This makes it possible to perform information processing across multiple modality. For example, in image retrieval, it becomes feasible to match the query (text) against images and find the most relevant images, because all of them are represented as vectors.