Current Challenges in NLP : Scope and opportunities

The objective of this section is to discuss evaluation metrics used to evaluate the model’s performance and involved challenges. Determine what data is necessary to build the model and whether it’s in shape for model ingestion. Questions should include how much data is needed, how the collected data will be split into test and training sets, and if a pre-trained ML model can be used. It will undoubtedly take some time, as there are multiple challenges to solve.

It indicates that how a word functions with its meaning as well as grammatically within the sentences. A word has one or more parts of speech based on the context in which it is used. Information extraction is one of the most important applications of NLP. It is used for extracting structured information from unstructured or semi-structured machine-readable documents. Large repositories of textual data are generated from diverse sources such as text steams on the web, communications through mobile and IoT devices.

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For comparison, AlphaGo required a huge infrastructure to solve a well-defined board game. The creation of a general-purpose algorithm that can continue to learn is related to lifelong learning and to general problem solvers. Informal phrases, expressions, idioms, and culture-specific lingo present a number of [newline]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.

NLP was revolutionized by the development of neural networks in the last two decades, and we can now use it for tasks we could not even imagine before. Another big open problem is reasoning about large or multiple documents. The recent NarrativeQA dataset is a good example of a benchmark for this setting. Reasoning with large contexts is closely related to NLU and requires scaling up our current systems dramatically, until they can read entire books and movie scripts.

Why is NLP important?

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. As text and voice-based data, as well as their practical applications, vary widely, NLP needs to include several different techniques for interpreting human native language.

Adding customized algorithms to specific NLP implementations is a great way to design custom models—a hack that is often shot down due to the lack of adequate research and development tools. Another challenge of NLP is dealing with the complexity and diversity of human language. Language is not a fixed or uniform system, but rather a dynamic and evolving one. It has many variations, such as dialects, accents, slang, idioms, jargon, and sarcasm. It also has many ambiguities, such as homonyms, synonyms, anaphora, and metaphors.

Domain-specific language

As they grow and strengthen, we may have solutions to some of these challenges in the near future. In the recent past, models dealing with Visual Commonsense Reasoning [31] and NLP have also been getting attention of the several researchers and seems a promising and challenging area to work upon. The world’s first smart earpiece Pilot will soon be transcribed over 15 languages. The Pilot earpiece is connected via Bluetooth to the Pilot speech translation app, which uses speech recognition, machine translation and machine learning and speech synthesis technology.

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