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Natural Language Processing (NLP) presents exciting language applications to users through making computers understand natural language and respond with its own language content.Â
Whether it’s conversing with bots, retrieving some information, or discovering how customers review products, NLP gracefully enables businesses to deliver quality services. It uses complex language algorithms that seemingly make computers talk and understand just like humans. Moreover, this capability comes with the power to process huge piles of text and digital documents in minutes.
Explore the top 4 of the many remarkable ways NLP serves humans. The article also sheds light on the working details of the underlying NLP process.
Applications of NLP
Following are top 4 mindblowing applications of NLP that enigmatically bring intelligence in computers and help them understand language as closely as humans do.
Speech recognition
Speech recognition, also known as speech-to-text, is an NLP application that converts voice data into text data as well as helps the computer understand this text input. It is used in voice assistants (such as Siri and Alexa) that reliably respond to user spoken prompts with answers in text or voice.Â
How it works: As a user speaks to the speech recognition system this voice input is converted to text data. Now, this text needs to be understood. For this an NLP language model breaks the text data into grammar and lexical components, and interprets the meaning of the sentence through context understanding. This can happen in two ways– rule based approach and supervised approach. In a rule based approach, an inventory of possible contexts is manually engineered to help the machine compare a sentence and reach the closest meaning. In a supervised approach, a machine learning model is trained on pre-annotated sentences that help machines learn to interpret meaning.Â
Using either of the two approaches, the speech recognition system understands the user prompt and takes an adequate response under its authority. This answer, either in voice or text, is created by the NLP algorithm using the same language intelligence it has in its capacity.
Parts of speech tagging
Parts of speech tagging is a task that assigns parts-of-speech– such as noun, pronoun, adjective, punctuation etc., to lexical terms of a text. POS tagging is an integral part of text analytics softwares and forms the basis of understanding the semantics of text. This means it’s a prerequisite for every NLP carrier system (an example of the use of POS tagging is in Word Sense Disambiguation application of NLP which is a core part of translation apps, information retrieval and text summarization).
How it works: The POS tagging system works on the theory of stochastic processes. Since, within a sentence, the part of speech of a current word greatly depends on the last word, hence it could be modeled as a stochastic process that defines an event as ‘random’ in nature and whose current state depends only on the previous state. Taking from this, the POS tagging assumes the tag of a target word as a random event and supposes that this tag depends on the previous word.Â
Word sense disambiguation– making sense of ambiguous words
Word sense disambiguation– WSD finds out the correct sense in which a word is used in a sentence. In natural language, ambiguities are caused due to the presence of words with the same spell but different meanings (homographs). A human mind processes homographs by understanding the context in which it’s presented. However, in computing, making sense of words and understanding the context is challenging.Â
How it works: WSD relies on a bulk of sense dictionaries, containing hundreds of thousands of words in related sense groups to help computers identify which word conveys what sense. A traditional WSD system uses a sense dictionary, whereas the more modern approach– supervised approach, uses a human sense-annotated text data for training of the machine.
WSD finds its applications in translation apps, speech recognition, text summarization, and information retrieval.
Keyword extraction
A keyword is defined as the most important word or phrase in a text. The importance of a word is reflected in that the whole context of discussion is centered around it. Keyword extraction is an NLP process that enables a machine to identify and extract keywords that are most relevant for a business. Using this, businesses can find customer insights on their products and services, within unstructured data such as emails, chats, social media posts and any other unorganized data. Finding keywords is also useful in marketing applications, where businesses want to know what phrases or words their customers use the most to reach their websites. These keywords, once identified, could be used to improve the search engine rankings of websites. Other applications of keyword research include discovering the trending topics and finding closely relevant documents.Â
How it works: some simple statistics such as word frequency, collocation and co-occurrence are used to identify keywords in a text. TF-IDF (term frequency- inverse document frequency) is a keyword extraction method that determines a word as a keyword that most appears in a document but less appears in the other documents. For this, TF-IDF multiplies word frequency with the inverse document frequency.Â
YAKE (Yet Another Keyword Extractor), a different but improved method, negates the requirement of external documents in the keyword extraction process and uses a unique methodology that solely relies on a single document. It builds a rich, five-statistics feature vector. These features include useful metrics such as: 1) the number times a word is used as Uppercase, 2) word position in the document (beginning, intermediate, or end), 3) term frequency, 4) term relatedness to the context (calculating co-location), and 5) term sentence frequency. Once this feature vector is created,keyword scores are computed against each word. The end product is a list of ranked keywords with scores.
NLP is the powerhouse of language applications!
In conclusion, the above four applications of NLP make it the powerhouse of language applications. We saw how NLP mimics human understanding of language in computers– parts of speech tagging, and word sense disambiguation. It is truly amazing that today people use NLP in their language apps to enable customized applications.Â
