2021 1 2 Origin AND Challenges OF NLP E23 NATURAL LANGUAGE PROCESSING 2. ORIGIN AND CHALLENGES OF

Some of the tasks such as automatic summarization, co-reference analysis etc. act as subtasks that are used in solving larger tasks. Nowadays NLP is in the talks because of various applications and recent developments although in the late 1940s the term wasn’t even in existence. So, it will be interesting to know about the history of NLP, the progress so far has been made and some of the ongoing projects by making use of NLP. 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.

• A typical American newspaper publishes a few hundred articles every day.
• All modules take standard input, to do some annotation, and produce standard output which in turn becomes the input for the next module pipelines.
• This approach is handy in spelling correction, text summarization, handwriting analysis, machine translation, etc.
• The marriage of NLP techniques with Deep Learning has started to yield results — and can become the solution for the open problems.
• Therefore, you need to ensure that your models can handle the nuances and subtleties of language, that they can adapt to different domains and scenarios, and that they can capture the meaning and sentiment behind the words.

For example, by some estimations, (depending on language vs. dialect) there are over 3,000 languages in Africa, alone. Natural Language Processing APIs allow developers to integrate human-to-machine communications and complete several useful tasks such as speech recognition, chatbots, spelling correction, sentiment analysis, etc. The main information overload, which poses a big problem to access a specific, important piece of information from vast datasets.

Natural Language Processing (NLP): 7 Key Techniques

There are other issues, such as ambiguity and slang, that create similar challenges. The main point is that the human language is a very complex and diversified mechanism. It varies greatly across geographical regions, industries, ages, types of people, etc.

This data

may exist in the form of tables, graphics, notations, page breaks, etc., which need to be

appropriately processed for the machine to derive meanings in the same way a human would

approach interpreting text. NLP models are larger and consume more memory compared to statistical ML models. Several intermediate and domain-specific models have to be maintained (e.g. sentence identification, pos tagging, lemmatisation, word representation models like TF-IDF, word2vec, etc.). Rebuilding all the intermediate NLP models for new data sets may cost more. The main challenge with language translation is not in translating words, but in understanding the meaning of sentences to provide an accurate translation.

Biggest Open Problems in Natural Language Processing

NLP stands for Natural Language Processing, which is a part of Computer Science, Human language, and Artificial Intelligence. It is the technology that is used by machines to understand, analyse, manipulate, and interpret human’s languages. It helps developers to organize knowledge for performing tasks such as translation, automatic summarization, Named Entity Recognition (NER), speech recognition, relationship extraction, and topic segmentation. However, in practice, translating NLP queries to formal DB queries or service request URL is quite complicated due to several factors. These could be the complex DB layouts with table names, columns, and constraints, etc., or the semantic gap between user vocabulary and DB nomenclature.

Ritter (2011) [111] proposed the classification of named entities in tweets because standard NLP tools did not perform well on tweets. They re-built NLP pipeline starting from PoS tagging, then chunking for NER. A language can be defined as a set of rules or set of symbols where symbols are combined and used for conveying information or broadcasting the information. Since all the users may not be well-versed in machine specific language, Natural Language Processing (NLP) caters those users who do not have enough time to learn new languages or get perfection in it. In fact, NLP is a tract of Artificial Intelligence and Linguistics, devoted to make computers understand the statements or words written in human languages.

Question answering

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