Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions IEEE Journals & Magazine

Machine Translation is generally translating phrases from one language to another with the help of a statistical engine like Google Translate. The challenge with machine translation technologies is not directly translating words but keeping the meaning of sentences intact along with grammar and tenses. In recent years, various methods have been proposed to automatically evaluate machine translation quality by comparing hypothesis translations with reference translations. In this research paper, a comprehensive literature review was undertaken in order to analyze Natural Language Processing (NLP) application based in different domains. Also, by conducting qualitative research, we will try to analyze the development of the current state and the challenge of NLP technology as a key for Artificial Intelligence (AI) technology, pointing out some of the limitations, risks and opportunities. In our research, we rely on primary data from applicable legislation and secondary public domain data sources providing related information from case studies.

This evaluator is pivotal, offering a nuanced understanding of the retrieved documents’ relevance and reliability. Based on its assessments, the evaluator can trigger different knowledge retrieval actions, enhancing the generated content’s robustness and accuracy. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai™, a next generation enterprise studio for AI builders. It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text. We see endless possibilities of NLP nowadays, ranging from chatbots and virtual assistants to sentiment analysis to language translation. These have already transformed many industries and elevated user experiences.

PROGRESS IN NATURAL LANGUAGE PROCESSING

A not-for-profit organization, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2024 IEEE – All rights reserved. An HMM is a system where a shifting takes place between several states, generating feasible output symbols with each switch. The sets of viable states and unique symbols may be large, but finite and known. Few of the problems could be solved by Inference A certain sequence of output symbols, compute the probabilities of one or more candidate states with sequences. Patterns matching the state-switch sequence are most likely to have generated a particular output-symbol sequence.

As NLP continues to evolve, these considerations will play a critical role in shaping the future of how machines understand and interact with human language. Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature

Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for

future research directions and describes possible research applications. Luong et al. [70] used neural machine translation on the WMT14 dataset and performed translation of English text to French text. The model demonstrated a significant improvement of up to 2.8 bi-lingual evaluation understudy (BLEU) scores compared to various neural machine translation systems.

Researchers from the University of Washington Developed a Deep Learning Method for Protein Sequence…

Standardize data formats and structures to facilitate easier integration and processing. Here’s a look at how to effectively implement NLP solutions, overcome data integration challenges, and measure the success and ROI of such initiatives. While Natural Language Processing has its limitations, it still offers huge and wide-ranging benefits to any business. And with new techniques and new technology cropping up every day, many of these barriers will be broken through in the coming years. Give this NLP sentiment analyzer a spin to see how NLP automatically understands and analyzes sentiments in text (Positive, Neutral, Negative).

But in NLP, though output format is predetermined in the case of NLP, dimensions cannot be specified. It is because a single statement can be expressed in multiple ways without changing the intent and meaning of that statement. Evaluation metrics are important to evaluate the model’s performance if we were trying to solve two problems with one model.

How to deal with the long tail problem poses a significant challenge to deep learning. Integrating NLP into existing IT infrastructure is a complex but rewarding endeavor. When executed strategically, it can unlock powerful capabilities for processing and leveraging language data, leading to significant business advantages. Measuring the success and ROI of these initiatives is crucial in demonstrating their value and guiding future investments in NLP technologies. Manuscripts should be submitted online at by registering and logging in to this website.

The Pilot earpiece will be available from September but can be pre-ordered now for $249. The earpieces can also be used for streaming music, answering voice calls, and getting audio notifications. The extracted information can be applied for a variety of purposes, for example to prepare a summary, to build databases, identify keywords, classifying text items according to some pre-defined categories etc.

The problem with naïve bayes is that we may end up with zero probabilities when we meet words in the test data for a certain class that are not present in the training data. Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of Natural Language Generation followed by presenting the history and evolution of NLP.

As early as 1960, signature work influenced by AI began, with the BASEBALL Q-A systems (Green et al., 1961) [51]. LUNAR (Woods,1978) [152] and Winograd SHRDLU were natural successors of these systems, but they were seen as stepped-up sophistication, in terms of their linguistic and their task processing capabilities. There was a widespread belief that progress could only be made on the two sides, one is ARPA Speech Understanding Research (SUR) project (Lea, 1980) and other in some major system developments projects building database front ends. The front-end projects (Hendrix et al., 1978) [55] were intended to go beyond LUNAR in interfacing the large databases. In early 1980s computational grammar theory became a very active area of research linked with logics for meaning and knowledge’s ability to deal with the user’s beliefs and intentions and with functions like emphasis and themes.

Challenges and Considerations in Natural Language Processing

Phonology is the part of Linguistics which refers to the systematic arrangement of sound. The term phonology comes from Ancient Greek in which the term phono means voice or sound and the suffix –logy refers to word or speech. Phonology includes semantic use of sound to encode meaning of any Human language. These are easy for humans to understand because we read the context of the sentence and we understand all of the different definitions. And, while NLP language models may have learned all of the definitions, differentiating between them in context can present problems. Though natural language processing tasks are closely intertwined, they can be subdivided into categories for convenience.

• In the tasks, words, phrases, sentences, paragraphs and even documents are usually viewed as a sequence of tokens (strings) and treated similarly, although they have different complexities.
• It is because a single statement can be expressed in multiple ways without changing the intent and meaning of that statement.
• Below we review the most common use cases of NLP to discover more about your business potential of transforming communication with NLP.
• Luong et al. [70] used neural machine translation on the WMT14 dataset and performed translation of English text to French text.

Apart from this, legal professionals apply NLP in regulatory compliance monitoring, surveillance transcript analysis, and legal research. Also, NLP can assist in transcription allowing physicians to dictate notes and minimize manual data entry. Clinical NLP systems can help with diagnoses, treatment plan composition, and personalized therapy recommendations. Uses NLP algorithms for the development of cancer treatment plans for its patients. A great example includes Coursera’s course recommendation system, which helps users find the best courses based on their interests.

Theoretical Quantum Approaches

Recently, Natural Language Processing (NLP) has witnessed pivotal advancements evolving various fields and transforming how we communicate and interact with computers by understanding human languages and dialects. However, many challenges still need to be addressed or improved to improve user performance. For example, mining software repositories have many open challenges, i.e., developing efficient techniques to handle and process massive research datasets, including source code, commit history, and bug reports. Similarly, researchers must develop state-of-the-art approaches to improve the performance of existing supervised and unsupervised learning approaches in classifying, clustering, and summarizing various social-media-based problems. This Special Issue aims to provide a comprehensive overview of the current trends, emerging technologies, and persistent challenges in NLP. It seeks to highlight the cutting-edge developments and address the hurdles the NLP community faces in this dynamic field.

Today’s NLP conversational AI systems are sophisticated enough to engage in authentic and contextually appropriate dialogues with users. Imagine a world where machines are able to understand what you say and how you feel; where you can talk to a computer, and it’ll respond; and where technology can sift through the text and summarize it for you. You don’t have to imagine anything — it’s a reality today, with the NLP adoption.

How to combine symbol data and vector data and how to leverage the strengths of both data types remain an open question for natural language processing. A language can be defined as a set of rules or set of symbols where symbols are combined and used for conveying information or broadcasting the information. Since all the users may not be well-versed in machine specific language, Natural Language Processing (NLP) caters those users who do not have enough time to learn new languages or get perfection in it. In fact, NLP is a tract of Artificial Intelligence and Linguistics, devoted to make computers understand the statements or words written in human languages. It came into existence to ease the user’s work and to satisfy the wish to communicate with the computer in natural language, and can be classified into two parts i.e. Natural Language Understanding or Linguistics and Natural Language Generation which evolves the task to understand and generate the text.

• Deep learning, when combined with other technologies (reinforcement learning, inference, knowledge), may further push the frontier of the field.
• It is used in customer care applications to understand the problems reported by customers either verbally or in writing.
• Athar’s work stands at the intersection “Sparse Training in DNN’s” and “Deep Reinforcemnt Learning”.
• The cue of domain boundaries, family members and alignment are done semi-automatically found on expert knowledge, sequence similarity, other protein family databases and the capability of HMM-profiles to correctly identify and align the members.
• Also, by conducting qualitative research, we will try to analyze the development of the current state and the challenge of NLP technology as a key for Artificial Intelligence (AI) technology, pointing out some of the limitations, risks and opportunities.

In summary, there are still a number of open challenges with regard to deep learning for natural language processing. Deep learning, when combined with other technologies (reinforcement learning, inference, knowledge), may further push the frontier of the field. Ambiguity is one of the major natural language processing challenges problems of natural language which occurs when one sentence can lead to different interpretations. In case of syntactic level ambiguity, one sentence can be parsed into multiple syntactical forms. Lexical level ambiguity refers to ambiguity of a single word that can have multiple assertions.