Natural language processing: state of the art, current trends and challenges SpringerLink

This is what we call homonyms, two or more words that have the same pronunciation but have different meanings. This can make tasks such as speech recognition difficult, as it is not in the form of text data. An NLP processing model needed for healthcare, for example, would be very different than one used to process legal documents.

Advanced practices like artificial neural networks and deep learning allow a multitude of

NLP techniques, algorithms, and models to work progressively, much like the human mind

does. As they grow and strengthen, we may have solutions to some of these challenges in the

near future. 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 to process

and respond to human language. A branch of machine learning AI, called Natural Language

Processing (NLP), allows machines to “understand” natural human language.

History of NLP

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. Informal phrases, expressions, idioms, and culture-specific lingo present a number of problems for NLP – especially for models intended for broad use. Because as formal language, colloquialisms may have no “dictionary definition” at all, and these expressions may even have different meanings in different geographic areas.

Therefore, they may inherit or amplify the biases, errors, or harms that exist in the data or the society. For example, NLP models may certain groups or individuals based on their gender, race, ethnicity, or other attributes. They may also manipulate, deceive, or influence the users’ opinions, emotions, or behaviors.

How does NLP work?

At present, it is argued that coreference resolution may be instrumental in improving the performances of NLP neural architectures like RNN and LSTM. Bi-directional Encoder Representations from Transformers (BERT) is a pre-trained model with unlabeled text available on BookCorpus and English Wikipedia. This can be fine-tuned to capture context for various NLP tasks such as question answering, sentiment analysis, text classification, sentence embedding, interpreting ambiguity in the text etc. [25, 33, 90, 148]. BERT provides contextual embedding for each word present in the text unlike context-free models (word2vec and GloVe).

Although NLP models are inputted with many words and definitions, one thing they struggle to differentiate is the context. The good news is that NLP has made a huge leap from the periphery of machine learning to the forefront of the technology, meaning more attention to language and speech processing, faster pace of advancing and more innovation. The marriage of NLP techniques with Deep Learning has started to yield results — and can become the solution for the open problems.

A third challenge of NLP is choosing and evaluating the right model for your problem. There are many types of NLP models, such as rule-based, statistical, neural, or hybrid ones. Each model has its own strengths and weaknesses, and may suit different tasks and goals.

Considering these metrics in mind, it helps to evaluate the performance of an NLP model for a particular task or a variety of tasks. The MTM service model and chronic care model are selected as parent theories. Review article abstracts target medication therapy management in chronic disease care that were retrieved from Ovid Medline (2000–2016).

Domain-specific language

Bayes’ Theorem is used to predict the probability of a feature based on prior knowledge of conditions that might be related to that feature. 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. 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.

We have come so far in NLP and Machine Cognition, but still, there are several challenges that

must be overcome, especially when the data within a system lacks consistency. Discourse Integration depends upon the sentences that proceeds it and also invokes the meaning of the sentences that follow it. Chunking is used to collect the individual piece of information and grouping them into bigger pieces of sentences. In English, there are a lot of words that appear very frequently like “is”, “and”, “the”, and “a”. Stop words might be filtered out before doing any statistical analysis.

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Homonyms – two or more words that are pronounced the same but have different definitions – can be problematic for question answering and speech-to-text applications because they aren’t written in text form. Usage of their and there, for example, is even a common problem for humans. They are an essential aspect of our lives (at least, for some of us), and it is fascinating to watch the evolution of games caused by AI. In particular, natural language processing is used to generate unique conversations and create exceptional experiences. Our game may develop in any direction thanks to natural language processing.

The ambiguity can be solved by various methods such as Minimizing Ambiguity, Preserving Ambiguity, Interactive Disambiguation and Weighting Ambiguity [125]. Some of the methods proposed by researchers to remove ambiguity is preserving ambiguity, e.g. (Shemtov 1997; Emele & Dorna 1998; Knight & Langkilde 2000; Tong Gao et al. 2015, Umber & Bajwa 2011) [39, 46, 65, 125, 139]. Their objectives are closely in line with removal or minimizing ambiguity. They cover a wide range of ambiguities and there is a statistical element implicit in their approach.

The framework requires additional refinement and evaluation to determine its relevance and applicability across a broad audience including underserved settings. 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. Even for humans this sentence alone is difficult to interpret without the context of

surrounding text. POS (part of speech) tagging is one NLP solution that can help solve the

problem, somewhat.

There are four stages included in the life cycle of NLP – development, validation, deployment, and monitoring of the models. Python is considered the best programming language for NLP because of their numerous libraries, simple syntax, and ability to easily integrate with other programming languages. But if there is any mistake or error, please post the error in the contact form.

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. 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.

• They all use machine learning algorithms and Natural Language Processing (NLP) to process, “understand”, and respond to human language, both written and spoken.
• However, many languages, especially those spoken by people with less access to technology often go overlooked and under processed.
• Semantic analysis focuses on literal meaning of the words, but pragmatic analysis focuses on the inferred meaning that the readers perceive based on their background knowledge.
• It also has many ambiguities, such as homonyms, synonyms, anaphora, and metaphors.

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