Natural Language Processing [NLP]Tasks And Applications

Natural Language Processing [NLP]Tasks And Applications

From the joint processing of computational science and applied linguistics,  Natural Language Processing  (NLP) was born, whose objective is none other than to make possible the computer-aided compression and processing of information expressed in human language, or what is the same, make communication between people and machines possible. Different programs exhibit different degrees of intelligent language processing. For example, a document finder can search for documents that contain the user-specified string of characters.

Regardless of whether or not that string has a meaning in a language or language. In this case, it would not be an application of the PLN. However, the same search engine could search the documents that communicate the user’s idea, regardless of the letters that speak it. In this case, without a doubt, it would be an excellent application of PLN since it would understand the idea communicated in the user request, the opinion expressed in each of the documents, and would be able to compare them.

For this reason, to go deeper into this topic, we present some tasks and applications of Natural Language Processing:

1.Speech To Text / Text To Speech

Speech to text or STT is based on the conversion of audio to text, and it is a task to value the audios, which, once converted into texts, can be processed with other PLN techniques. Once processed, it is possible to return audio using the reader to audio conversion (Text To Speech or TTS). Both tasks, STT and TTS, have become very relevant with conversational systems with a high level of quality, such as Siri, Alexa, OK Google, Cortana, etc.

2.Questioning And Answering (Q&A)

Q&A is the task of answering questions based on information obtained from different resources. It is an important task for dialogue systems such as chatbots and improved search systems (Information Retrieval). The new Deep Learning systems are allowing a substantial improvement in this task. In a project on Smart Tourism, a Questioning and Answering system was developed, trained with comments on Andalusian tourism resources (hotels, restaurants, beaches, museums,.), and allowed locating tourist resources from many varied questions. Earlier, we mentioned Information Retrieval systems, and these search systems were one of the first Natural Language Processing systems to be widely adopted. 

There are two fundamental steps in a search engine: Inverted Index Generation. The inverted index allows saving for each word the documents in which it appears so that the search of the documents that contain a word is very fast. Document ranking. Once all the documents containing the words we are looking for have been located, it is necessary to decide how to display them (order of relevance). Here Google introduced innovation with its technique called PageRank.

3.Automatic Translation

Automatic translation allows you to translate a text from one language to another. This task gained significant momentum thanks to the corpus of texts translated between two languages ​​(called “parallel corpus”) provided by the European Union parliament. Compared to the first systems of the 1950s, machine translation is currently a Natural Language Processing task that has achieved a high level of quality. Example of this, applications such as iTranslate Converse for iOS or Microsoft Translator App.

4. Information Extraction

Information extraction is obtaining a predefined set of fields from a text in free format. It can be seen as the generation of a database from unstructured documents. For example, get different data from a PDF about weld inspections, extract: welder name, weld characteristics, etc.

5 Document Classification: How It Works

The document classification task consists of training a system to learn to classify texts from a set of already classified texts. In most cases, these systems work quite well, achieving classification qualities (e.g., accuracy) higher than 95%. We explained how an automatic document classifier works using PLN and Machine Learning techniques on a set of elements to sort them by classes or categories in a previous post.

Also Read: How To Work SEO On Voice Devices


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