The Power of Natural Language Processing

Datasets used in NLP and various approaches are presented in Section 4, and Section 5 is written on evaluation metrics and challenges involved in NLP. Natural language processing (NLP) is an interdisciplinary subfield of computer science and linguistics. 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.

As they grow and strengthen, we may have solutions to some of these challenges in the near future. An NLP processing model needed for healthcare, for example, would be very different than one used to process legal documents. These days, however, there are a number of analysis tools trained for specific fields, but extremely niche industries may need to build or train their own models. The following is a list of some of the most commonly researched tasks in natural language processing.

Major Challenges of Natural Language Processing (NLP)

By this time, work on the use of computers for literary and linguistic studies had also started. 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.

This approach seems more natural, and a similar strategy is already explored in Prakash et al. (2021). This means simultaneous data streams (e.g., physiological, physical, psychological, social, or contextual) that are assessed over long periods in daily life. NLP is an exciting and rewarding discipline, and has potential to profoundly impact the world in many positive ways. Unfortunately, NLP is also the focus of several controversies, and understanding them is also part of being a responsible practitioner. For instance, researchers have found that models will parrot biased language found in their training data, whether they’re counterfactual, racist, or hateful. Moreover, sophisticated language models can be used to generate disinformation.

Natural language processing

For example, by some estimations, (depending on language vs. dialect) there are over 3,000 languages in Africa, alone. While several works follow the original ideas of transformers (vocabulary definition, pretraining, and fine-tuning) using categorical problems in nlp data as input, other proposals use numeric values as input. The need to handle such data is mainly derived from the current trend of using mobile health technology—mHealth (e.g., wearables) to assess multifeature longitudinal health data.

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. 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.

CapitalOne claims that Eno is First natural language SMS chatbot from a U.S. bank that allows customers to ask questions using natural language. Customers can interact with Eno asking questions about their savings and others using a text interface. This provides a different platform than other brands that launch chatbots like Facebook Messenger and Skype.

• If you are interested in working on low-resource languages, consider attending the Deep Learning Indaba 2019, which takes place in Nairobi, Kenya from August 2019.
• Word embedding creates a global glossary for itself — focusing on unique words without taking context into consideration.
• Discriminative methods are more functional and have right estimating posterior probabilities and are based on observations.
• As far as categorization is concerned, ambiguities can be segregated as Syntactic (meaning-based), Lexical (word-based), and Semantic (context-based).
• Even though evolved grammar correction tools are good enough to weed out sentence-specific mistakes, the training data needs to be error-free to facilitate accurate development in the first place.

In other words, this process indicates the concepts and relations (part of a graph) that are important to the modeling of outcomes. However, these outcomes are usually complemented with other types of daily-life assessments using technology-reported outcomes (TechRO, e.g., wearables). Thus, longitudinal health data can be modelled with a set of vocabularies beyond the simple use of diagnoses and medication (Li et al. 2020; Rao et al. 2022a; Fouladvand et al. 2021; Boursalie et al. 2021). Approaches that employ multiple vocabularies (Meng et al. 2021) usually sum or concatenate their inputs to generate a unique data stream (Li et al. 2020; Rao et al. 2022a).

NLP for low-resource scenarios

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. The last group also relies on attention weights but uses additional techniques.

In case of machine translation, encoder-decoder architecture is used where dimensionality of input and output vector is not known. Neural networks can be used to anticipate a state that has not yet been seen, such as future states for which predictors exist whereas HMM predicts hidden states. Transformers are state-of-the-art technology to support diverse Natural Language Processing (NLP) tasks, such as language translation and word/sentence predictions. The main advantage of transformers is their ability to obtain high accuracies when processing long sequences since they avoid the vanishing gradient problem and use the attention mechanism to maintain the focus on the information that matters. These features are fostering the use of transformers in other domains beyond NLP.

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