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

Ambiguity is one of the major 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. Each of these levels can produce ambiguities that can be solved by the knowledge of the complete sentence. 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].

This involves having users query data sets in the form of a question that they might pose to another person. The machine interprets the important elements of the human language sentence, which correspond to specific features in a data set, and returns an answer. These are the types of vague elements that frequently appear in human language and that machine learning algorithms have historically been bad at interpreting. Now, with improvements in deep learning and machine learning methods, algorithms can effectively interpret them.

Six Important Natural Language Processing (NLP) Models

Several companies in BI spaces are trying to get with the trend and trying hard to ensure that data becomes more friendly and easily accessible. But still there is a long way for this.BI will also make it easier to access as GUI is not needed. Because nowadays the queries are made by text or voice command on smartphones.one of the most common examples is Google might tell you today what tomorrow’s weather will be. But soon enough, we will be able to ask our personal data chatbot about customer sentiment today, and how we feel about their brand next week; all while walking down the street. Today, NLP tends to be based on turning natural language into machine language. But with time the technology matures – especially the AI component –the computer will get better at “understanding” the query and start to deliver answers rather than search results.

Various researchers (Sha and Pereira, 2003; McDonald et al., 2005; Sun et al., 2008) [83, 122, 130] used CoNLL test data for chunking and used features composed of words, POS tags, and tags. Here the speaker just initiates the process doesn’t take part in the language generation. It stores the history, structures the content that is potentially relevant and deploys a representation of what it knows. All these forms the situation, while selecting subset of propositions that speaker has. The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches. Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach.

Common Natural Language Processing (NLP) Task:

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.

• There are four stages included in the life cycle of NLP – development, validation, deployment, and monitoring of the models.
• There are particular words in the document that refer to specific entities or real-world objects like location, people, organizations etc.
• As the technology evolved, different approaches have come to deal with NLP tasks.
• Whether it’s being used to quickly translate a text from one language to another or producing business insights by running a sentiment analysis on hundreds of reviews, NLP provides both businesses and consumers with a variety of benefits.
• Earlier machine learning techniques such as Naïve Bayes, HMM etc. were majorly used for NLP but by the end of 2010, neural networks transformed and enhanced NLP tasks by learning multilevel features.

Natural language processing (NLP) stands halfway between computer science computational linguistics, and it is dedicated to the conversion of written and spoken natural human languages into structured mineable data. Through the combination of linguistic, statistical and AI methods NLP can be used either to determine the meaning of a text or even to produce a human-like response. NLP is already part development in natural language processing of our everyday life as it is widely implemented in our computer software or in our mobile phones. As hospitals produce a large amount of written free-text data, NLP may play an important role to develop tool for clinical decision support, evidence-based medicine, or even research. Indeed, NLP application in the field of medical informatics has received an increasing attention in the recent years.

Statistical NLP, machine learning, and deep learning

Next, we discuss some of the areas with the relevant work done in those directions. We first give insights on some of the mentioned tools and relevant work done before moving to the broad applications of NLP. Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) have not been needed anymore. In NLP, such statistical methods can be applied to solve problems such as spam detection or finding bugs in software code. Overall, NLP is a rapidly evolving field that has the potential to revolutionize the way we interact with computers and the world around us. But, as will appear most likely at the end of my essay, we seem to be getting there and it might be sooner than we think.

Earlier machine learning techniques such as Naïve Bayes, HMM etc. were majorly used for NLP but by the end of 2010, neural networks transformed and enhanced NLP tasks by learning multilevel features. Major use of neural networks in NLP is observed for word embedding where words are represented in the form of vectors. Initially focus was on feedforward [49] and CNN (convolutional neural network) architecture [69] but later researchers adopted recurrent neural networks to capture the context of a word with respect to surrounding words of a sentence. LSTM (Long Short-Term Memory), a variant of RNN, is used in various tasks such as word prediction, and sentence topic prediction.

Why Does Natural Language Processing (NLP) Matter?

Shortly after this, in 1952, the Hodgkin-Huxley model showed how the brain uses neurons in forming an electrical network. These events helped inspire the idea of Artificial Intelligence (AI), Natural Language Processing (NLP), and the evolution of computers. Although natural language processing might sound like something out of a science fiction novel, the truth is that people already interact with countless NLP-powered devices and services every day.

But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes. The first patents for “translating machines” were applied for in the mid-1930s. One proposal, by Georges Artsrouni was simply an automatic bilingual dictionary using paper tape. It included both the bilingual dictionary, and a method for dealing with grammatical roles between languages, based on Esperanto.

Statistical approach

The feed-forward neural network has no cycles or loops, and is quite different from the recurrent neural networks. In 1958, the programming language LISP (Locator/Identifier Separation Protocol), a computer language still in use today, was released by John McCarthy. In 1964, ELIZA, a “typewritten” comment and response process, designed to imitate a psychiatrist using reflection techniques, was developed. (It did this by rearranging sentences and following relatively simple grammar rules, but there was no understanding on the computer’s part.) Also in 1964, the U.S. National Research Council (NRC) created the Automatic Language Processing Advisory Committee, or ALPAC, for short. This committee was tasked with evaluating the progress of natural language processing research.

Starting in the late 1980s, however, there was a revolution in NLP with the introduction of machine learning algorithms for language processing. Increasingly, however, research has focused on statistical models, which make soft, probabilistic decisions based on attaching real-valued weights to the features making up the input data. The cache language models upon which many speech recognition systems now rely are examples of such statistical models. Such models are generally more robust when given unfamiliar input, especially input that contains errors (as is very common for real-world data), and produce more reliable results when integrated into a larger system comprising multiple subtasks.

Evaluation metrics and challenges

Recently, researchers from Carnegie Mellon & Google developed an Attention Network based architecture called the XLNet. At the same time, a Chinese company published another Attention Network based model called ERNIE 2.0, they claim to outperform both BERT and XLNet in 16 different tasks. The main benefit of NLP is that it improves the way humans and computers communicate with each other. The most direct way to manipulate a computer is through code — the computer’s language. By enabling computers to understand human language, interacting with computers becomes much more intuitive for humans.

Top Natural Language Processing (NLP) Techniques

Initially, the data chatbot will probably ask the question ‘how have revenues changed over the last three-quarters? But once it learns the semantic relations and inferences of the question, it will be able to automatically perform the filtering and formulation necessary to provide an intelligible answer, rather than simply showing you 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.

The third objective of this paper is on datasets, approaches, evaluation metrics and involved challenges in NLP. Section 2 deals with the first objective mentioning the various important terminologies of NLP and NLG. Section 3 deals with the history of NLP, applications of NLP and a walkthrough of the recent developments. 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. Up to the 1980s, most NLP systems were based on complex sets of hand-written rules.