Natural Language Processing: Challenges and Future Directions SpringerLink

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. Phonology is the part of Linguistics which refers to the systematic arrangement of sound. The problems in nlp 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 the most common challenges that are faced in NLP that can be easily resolved.

Given my involvement in NLP, I would like to address the question of whether the narrowly defined CL is relevant to NLP. However, the answer is not so straightforward, and requires us to examine the degree to which the representations used to describe language as a system are relevant to the representations used for processing language. CL, which focuses on formal/computational description of languages as a system, is expected to bridge broader fields of linguistics with the lower disciplines, which are concerned with processing of language. Also, NLP has support from NLU, which aims at breaking down the words and sentences from a contextual point of view.

NLP: Then and now

There are particular words in the document that refer to specific entities or real-world objects like location, people, organizations etc. To find the words which have a unique context and are more informative, noun phrases are considered in the text documents. Named entity recognition (NER) is a technique to recognize and separate the named entities and group them under predefined classes. But in the era of the Internet, where people use slang not the traditional or standard English which cannot be processed by standard natural language processing tools. Ritter (2011) [111] proposed the classification of named entities in tweets because standard NLP tools did not perform well on tweets. Pragmatic level focuses on the knowledge or content that comes from the outside the content of the document.

NLP hinges on the concepts of sentimental and linguistic analysis of the language, followed by data procurement, cleansing, labeling, and training. Yet, some languages do not have a lot of usable data or historical context for the NLP solutions to work around with. Like the culture-specific parlance, certain businesses use highly technical and vertical-specific terminologies that might not agree with a standard NLP-powered model. Therefore, if you plan on developing field-specific modes with speech recognition capabilities, the process of entity extraction, training, and data procurement needs to be highly curated and specific. However, if we need machines to help us out across the day, they need to understand and respond to the human-type of parlance. Natural Language Processing makes it easy by breaking down the human language into machine-understandable bits, used to train models to perfection.

Challenges for low-resource languages:

These two cycles are required to treat language pairs like Japanese and English. Differences are abundant when we treat languages that belong to very different language families (Tsujii 1982). In this definition, I take research on parsing as part of NLP, since it is concerned with processing of language. Research on parsing algorithms, however, may be quite different in nature from the engineering side of NLP. Therefore, even for parsing using feature-based formalisms, issues of disambiguation and how to handle the explosion of ambiguities remained major issues for NLP.

Another interesting approach is proposed in Peng et al. (2021), which uses medical ontologies to augment the sequential information of patients. Thus, this approach uses an ontology encoder to map the ontology information to a vector. First, they are pretrained using strategies such as Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) (Devlin 2018).

Significant progresses were made possible by large treebanks (for example, Fujisaki (1984)). Chomsky explicitly avoided problems related with interpretation and treated language as a closed system. Apart from the equality of information, the interlingual approach assumed that the language-independent representation consists only of language-independent lexemes. This involved implausible work of defining a set of language-independent concepts. The left side of this figure is the transfer with a pre- and post-cycle of adjustment.

A broader concern is that training large models produces substantial greenhouse gas emissions. NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis. Businesses use NLP to power a growing number of applications, both internal — like detecting insurance fraud, determining customer sentiment, and optimizing aircraft maintenance — and customer-facing, like Google Translate. Data augmentation is a data pre-processing strategy that automatically creates new data without collecting it explicitly.

One of the challenges we try to explain to customers is that it’s not “done” yet. We’re discovering things as we go, and that’s the case across all industries. So with all of these conditions, you need to read the notes of the discussion between the doctors and nurses to understand patients at risk and their needs. Using medical NLP, clinical protocols can be automated and additional insights gained. The adoption of Natural Language Processing (NLP) in healthcare can be transformational as it facilitates the interpretation of large amounts of data in order to gain valuable insights and make more accurate decisions that lead to better patient care.

This editorial first provides an overview of the field of NLP in terms of research grants, publication venues, and research topics. Benefits and impact   Another question enquired—given that there is inherently only small amounts of text available for under-resourced languages—whether the benefits of NLP in such settings will also be limited. Stephan vehemently disagreed, reminding us that as ML and NLP practitioners, we typically tend to view problems in an information theoretic way, e.g. as maximizing the likelihood of our data or improving a benchmark. Taking a step back, the actual reason we work on NLP problems is to build systems that break down barriers. We want to build models that enable people to read news that was not written in their language, ask questions about their health when they don’t have access to a doctor, etc. Emotion   Towards the end of the session, Omoju argued that it will be very difficult to incorporate a human element relating to emotion into embodied agents.

Nevertheless, ongoing research into the integration of extracted information has started to reassess the importance of linguistic structures. For example, claims about an event extracted from different articles often contradict each other. As such, techniques for measuring the credibility or reliability of claims are crucial. A CFG skeleton, which also was derived from the HPSG grammar, was used to check whether sequences of supertags chosen by the first phase could reach a successful derivation tree. The supertagger did not build actual parse trees explicitly to check whether a chosen sequence could reach legitimate derivation trees or not.

Finally, there is NLG to help machines respond by generating their own version of human language for two-way communication. Fan et al. [41] introduced a gradient-based neural architecture search algorithm that automatically finds architecture with better performance than a transformer, conventional NMT models. They tested their model on WMT14 (English-German Translation), IWSLT14 (German-English translation), and WMT18 (Finnish-to-English translation) and achieved 30.1, 36.1, and 26.4 BLEU points, which shows better performance than Transformer baselines.

However, the analysis phase in this approach becomes clumsy and convoluted (Tsujii, Nakamura, and Nagao 1984; Tsujii et al. 1988). On the negative side, NLP based on large language models is increasingly separating itself from other research disciplines that involve the study of language. The black box nature of NN and DL also makes the analytical methods way of assessing NLP systems difficult. We have witnessed the rapid progress and significant changes that neural network (NN) models and deep learning (DL) have brought to the field of NLP.

• Apart from strategies representing the temporal notion, longitudinal health data present issues such as sparse and irregular time assessment intervals.
• But in first model a document is generated by first choosing a subset of vocabulary and then using the selected words any number of times, at least once without any order.
• Without analysis based on theories provided by other language-related disciplines, erratic and unexpected behaviors of NN-based NLP systems will remain and limit potential applications.

An iterative process is used to characterize a given algorithm’s underlying algorithm that is optimized by a numerical measure that characterizes numerical parameters and learning phase. Machine-learning models can be predominantly categorized as either generative or discriminative. Generative methods can generate synthetic data because of which they create rich models of probability distributions. Discriminative methods are more functional and have right estimating posterior probabilities and are based on observations. Srihari [129] explains the different generative models as one with a resemblance that is used to spot an unknown speaker’s language and would bid the deep knowledge of numerous languages to perform the match. Discriminative methods rely on a less knowledge-intensive approach and using distinction between languages.

A key question here—that we did not have time to discuss during the session—is whether we need better models or just train on more data. So, for building NLP systems, it’s important to include all of a word’s possible meanings and all possible synonyms. Text analysis models may still occasionally make mistakes, but the more relevant training data they receive, the better they will be able to understand synonyms. These are easy for humans to understand because we read the context of the sentence and we understand all of the different definitions.