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In Detail Speech Recognition, Natural & Technical Language Processing

An Introduction to Technical Language Processing Natural Language Processing (NLP) and Technical Language Processing (TLP) are two related terms of modern bot...

Written by Daniel Roncaglia · 3 min read >
Technical Language Processing

An Introduction to Technical Language Processing

Natural Language Processing (NLP) and Technical Language Processing (TLP) are two related terms of modern bot that help us interpret and classify search words better. These technologies are quite beneficial and provide firms with enhanced analysis capabilities. We will look into NLP and TLP to see what they’re good for and how they might be used in enterprises.

Let’s discuss technical language processing

Before discussing speech and natural language processing, let’s see the difference between speech recognition and natural language processing. We are here to tell you the difference between speech recognition and natural language processing.

#Speech RecognitionNatural Language Processing
1Speech recognition is a program to identify words spoken and convert them into readable text by humans.NLP is the latest technology for speech recognition which speeds up the process.
2Speech recognition is simply the ability of software to recognize speech. The software must recognize anything that a person says in a language of their choice.NLP means that the computer must accurately determine a user’s intentions from normally written language.
3Speech recognition is processing the speech to convert it into text.NLP is processing the text to understand the meaning of the text.
4Speech recognition is used for dictation tasks, speech to text applications, virtual assistants, voice biometrics.NLP is used to perform automatic summarization, topic segmentation, relationship extraction, information retrieval areas.
5Speech recognition used our signal processing, phonetics, word recognition area.NLP is used on morphology, grammar, and passing semantics pragmatics, discourse dialogue, spoken language understanding.

Introducing of Technical Language Processing

Technical language is a subset of natural language. Technical language processing is a human-in-the-loop, iterative approach to tailor NLP tools to engineering data. Technical Language Processing (TLP) builds on natural language processing (NLP) by employing an iterative process to customize NLP tools for a given business or industry. It uses extensive domain-based taxonomies and data dictionaries to ensure that the system recognizes all important technical terms, abbreviations, and acronyms that may appear in a document or search term. TLP can uncover ‘hidden’ technical knowledge, which can then provide valuable insights by using your company’s data assets.

TLP is just natural language with a focus or bias on technical aspects or concepts; technical language and code are everywhere.

Code Detection API can automate tagging, manual search, finding text or code, etc. API makes is it’s searchable, indexable, and visually aesthetic. And you know, it’s just a better experience than doing all it manually today. Today coders around the globe can benefit from this cloud API.

TLP Can Help Determining the Programming Language

speech and language processing

TLP can easily detect if the text you have is a bit of normal, just sort of code? Is this just a normal conversation? Or is this technical language? And if it is technical language, is their code inside it. Not only does it determine it also explains the below things. What language is it, and what tags and labels can be associated with it? So once you determine the language, whether it is Dart, Angular, PHP, or any language. And TLP can format it and highlight syntax according to programming language. And then, of course, cloud API attempts to describe it through a couple of keywords.

Source: https://codedetectionapi.runtime.dev/

Use case of Technical Language Processing

speech recognition and natural language processing
  • A simple method for reliably detecting code in the text
  • Improve search results in your app by tagging and indexing snippets as code rather than text
  • Scrub data pipelines to identify code distinctly from text
  • Large data sets with the most likely code language and other meta-info

How CodeDetectionAPI can help businesses

Code Detection is the industry’s bug-free technical language processing APIs provide. They are a data and microservice, API provider. CodeDetectionAPI is an API marketplace that provides 70 different API, and they are adding new APIs every day, including this code detection API.

The Code Detection API determines whether a given string of text is code and, if so, returns information such as the string’s language, relevant tags, and a preprocessed version. The API is powered by Pieces’ own machine learning models, which are constantly improving as the API’s usage grows.

The nature of work has shifted away from perhaps real cubicles and toward more virtual employment. Maybe we’re still not going into the office or are just getting back into it. However, in terms of teams and development, we are more geographically distributed. Development and cooperation are made considerably easier thanks to technologies and APIs.

Using Code detection API, developers can use the Code Detection API to improve communication between developers working remotely within a team. For example, they often need to exchange messages with code snippets. How developers and teams interact with that code, describe that code, evolve that code, and then bake that into an automatic. And so, you know, search, suggestion, reuse, in collaboration are going to be, you know, massive capabilities at the API level that end up in experiences that we don’t have today, but we should have tomorrow.

How Voice Recognition Technology Works

The API will be broadly applicable to:

  • Developers working on publishing, chat/messaging, productivity, design, and lightweight coding apps who wish to intelligently format, render, and tag text that has been determined to be code in the way a user would expect code to be rendered.
  • Teams in AI, Machine Learning, Data Science, and Analytics need to process, label, enhance, and organize vast data streams with code and plain language.
  • “We all know software is devouring the globe, and as a result, the volume of code in the world is rising tremendously,” Tsavo Knott, Co-Founder, and CEO of Pieces, remarked. While Natural Language Processing has been around for a long time, there aren’t many APIs that process ‘Technical Language’ or code.

Need of natural language processing and Technical Language Processing Together

Today AI-powered technology employs NLP and TLP to accomplish two major objectives:

  • Deciphering and comprehending the intent of a user’s search phrases
  • Classifying any ingested data accurately.

These two objectives ensure that users get the most relevant search results possible, even if they don’t have access to technical terms or phrases. This can improve efficiency, analytics, and various other corporate functions.

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