Challenges and Solutions in Natural Language Processing NLP by samuel chazy Artificial Intelligence in Plain English

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. Initially, the data chatbot will probably ask the question ‘how have revenues changed over the last three-quarters?

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. We then discuss in detail the state of the art applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP. While background, domain knowledge and frameworks (e.g. algorithms and tools) are the critical components of the NLP system, it is not a simple and easy task of making machines to understand natural human language. The process includes several activities such as pre-processing, tokenisation, normalisation, correction of typographical errors, Named Entity Reorganization (NER), and dependency parsing. To attain high-quality models, NLP performs an in-depth analysis of user inputs like lexical analysis, syntactic analysis, semantic analysis, discourse integration, and pragmatic analysis, etc.

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Furthermore, cultural slang is constantly morphing and expanding, so new words pop up every day. 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 and Natural Language Processing (NLP) to process, “understand”, and respond to human language, both written and spoken. Depending on the personality of the author or the speaker, their intention and emotions, they might also use different styles to express the same idea. Some of them (such as irony or sarcasm) may convey a meaning that is opposite to the literal one.

For example, CONSTRUE, it was developed for Reuters, that is used in classifying news stories (Hayes, 1992) [54]. It has been suggested that many IE systems can successfully extract terms from documents, acquiring relations between the terms is still a difficulty. PROMETHEE is a system that extracts lexico-syntactic patterns relative to a specific conceptual relation (Morin,1999) [89]. IE systems should work at many levels, from word recognition to discourse analysis at the level of the complete document. An application of the Blank Slate Language Processor (BSLP) (Bondale et al., 1999) [16] approach for the analysis of a real-life natural language corpus that consists of responses to open-ended questionnaires in the field of advertising.

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This phase scans the source code as a stream of characters and converts it into meaningful lexemes. For Example, intelligence, intelligent, and intelligently, all these words are originated with a single root word “intelligen.” In English, the word “intelligen” do not have any meaning. NLU mainly used in Business applications to understand the customer’s problem in both spoken and written language. LUNAR is the classic example of a Natural Language database interface system that is used ATNs and Woods’ Procedural Semantics. It was capable of translating elaborate natural language expressions into database queries and handle 78% of requests without errors.

Each text comes with different words and requires specific language skills. Choosing the right words depending on the context and the purpose of the content, is more complicated. This is where contextual embedding comes into play and is used to learn sequence-level semantics by taking into consideration the sequence of all words in the documents. This technique can help overcome challenges within NLP and give the model a better understanding of polysemous words. Yes, words make up text data, however, words and phrases have different meanings depending on the context of a sentence.

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