Why Auto-Punctuation Is Crucial to Natural Language Processing?

Written by Flashscribe

Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables computers to understand and process human languages. The world’s fastest supercomputer Summit with the High Performance Linpack (HPL) of 148.6 petaFLOPS can process structured data at the speed of 200 quadrillion per second. However, AI demands computers to convert unstructured information into structured data like that of a human brain for Natural Language Processing (NLP) and Natural Language Understanding (NLU) with higher accuracy.  

Let’s explain this with a simple example. Do you remember your comprehension worksheets from grade 6? You read a passage and then answered a set of questions that are related to the passage. It was certainly an integral part of your academics for a valid reason. It’s high time that you understood that the reading comprehension worksheets distributed during your language classes require several critical thinking skills including deductive reasoning, logical inference, sequential analysis, tonal awareness, and an understanding of scope which are also a part of NLU and NLP.  

Computers apply Machine Learning and NLP concepts to compare and analyse specific patterns in a set of training data. More often, languages like English have context-based grammatical rules that are both complex and inconsistent. As a result, semantic parsing is a huge challenge for computers. 

A practical solution could include breaking a sentence into simpler segments and extracting meaning from it. Punctuations aid NLP systems like speech engines to extract the meaning of the given text through text categorization, syntactic parsing and Part-Of-Speech tagging.  

Here’s a sentence for a speech engine to decipher.  

London was founded by the Romans, who named it Londinium.” 

How does the human brain understand this sentence? 

The sentence is split into the subject, London“ and predicate, “was founded by the Roman, who named it Londinium. The predicate is further split into object, “Romans” and clause, who named it Londinium”.  The different parts of speech marked by 6 major punctuations to appropriately highlight the contextual meaning of the given text for better understanding. 

The 6 major punctuations are inevitable for improving the parsing of a language including  

  1. Full-stop (.)
  2. Comma (,)
  3. Question mark (?)
  4. Exclamation mark (!)
  5. Semi-colon (;)
  6. Colon (:)

First, a sentence segmentation model helps split a sentence into smaller parts based on the punctuation marks. Then, punctuations act as sperate tokens in the process of word tokenization since each punctuation has its own meaning. Later, each word token is fed into a pre-trained part-of-speech classification model. The model applies Machine Learning’s (ML) concept of pattern recognition to identify the part of speech of each word token in a sentence. It will eventually help the NLP engine to understand and process the unstructured information into structured data for real-world applications.  

For instance, if you’re using a speech engine like Flashscribe to transcribe your speech, then you may or may not notify the punctuation marks when you’re speaking or recording speeches on the app. Our auto-punctuation feature makes a huge difference when it comes to improving the NLP capabilities of a speech engine and transcribing more accurately.  

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