Human language is an abstract and complex concept. From different tones to different cultural perceptions, even humans have a difficult time understanding one another. While not understanding it completely ourselves, we have decided to teach what language means to computers, and through this, we have entered a completely new world. We do this through Natural Language Processing (NLP).

NLP is the branch of Artificial Intelligence that gives machines the ability to read, understand and derive meaning from human languages. NLP combines techniques from fields such as linguistics and computer science to decipher language structure in order to create models that can comprehend and separate significant details from text and speech.

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Every day human beings share and receive data through social networks, creating a vast amount of data that is free and readily available for use. This data is extremely important to NLPs because it can help machines analyze human behavior and customer habits. Data analysts and researchers utilize this data to give machines the ability to mimic human language and behavior.

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Natural Language Processing is the application of taking unstructured data and converting it into structured data. Computers have to analyze vast amounts of data and learn through countless examples what the meanings of specific words, phrases, and sentences are.

How does it work?

There are many steps when it comes to Natural Language Processing, and here are a couple: let’s say you start with an article:

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First, we break down the article into meaningful sentences or topics, this is called segmentation.
Then we break down the text into tokens and explain each individual word to the computer which is called tokenization.
Next, we do a technique called stemming, which is used to extract a word’s origin by removing prefixes, affixes, and suffixes.
Now, we group together different inflected forms of a word by removing the word’s relation to gender, time, or mood- this is called lemmatization.
Now we remove any parts of speech in the sentence, such as nouns, verbs, adjectives, etc- which is called Parts of Speech (POS) Tagging.
Finally, we classify any entities mentioned in the text into categories through a process called Named Entity Recognition such as “People,” “Locations,” “Organizations,” and so on.

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What are some application examples?

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We use NLPs every day, and their uses have become integral to our everyday life.

Autocorrect: Certain NLPs are programmed to correct spellings and errors while typing text. It is completely based on NLP that compares the words in the vocabulary dictionary and the typed words on the keyboard.
Search Engine Results: Search engines now analyze people’s intent when they search for information. Search autocomplete is a function that predicts what you might be searching for through NLPs. Another tool driven by NLPs is smart e-commerce search which learns about customer intentions with every interaction and offers related results, encouraging the customer to buy more.
Digital Assistants (ie: Alexa, Siri, Google Home): Smart assistants use voice recognition to understand our everyday questions and requests, and then utilize natural language generation to answer the queries. They learn from experience through applications of NLP to provide better and more personalized assistance.
Customer Service Automation: NLPs can be used to detect sentiments and keywords in emails and responses. These responses can receive automated responses or be assigned to the relevant team depending on what the NLP picks up. This means that customer claims aren’t getting lost and that businesses can learn more about what they need to change based on keywords.
Survey Analytics: Large amounts of information can be impossible to analyze and sort through manually. NLPs utilize tools such as sentiment analysis and feedback analysis which can scan for positive, negative, or neutral emotions that allow companies to draw actionable insights from the data received.
Audio Transcription: NLPs utilize cutting-edge machine-learning techniques that allow machines to understand human language and transcribe audio into text.
Audio Intelligence: This is a step above Audio Transcription, which is a transcription of captured audio that produces sentimental and contextual analysis to qualify conversations.
Summarization: Large-scale texts can be broken down into meaningful topics or sentences that allow readers or consumers to read a summary and save time, energy, and effort while still understanding the main ideas.
Text Analytics: also known as text mining which is used for deeper insights such as identifying a pattern or trend from an unstructured text.

NLP, NLU and NLG

Now that we have a basic understanding of what NLP is- let’s take it a step further.
Natural Language Understanding (NLU) and Natural Language Generation (NLG) are both subsets of NLP.

NLU is primarily focused on machine reading comprehension through context and grammar, enabling it to determine the intentions behind a particular sentence.
NLG focuses on the construction of text in English or other languages, by a machine and based on a given set of data.

NLP using the AI Studio:

When you consider how complicated human language is and how differently each individual uses it- it is extremely difficult to create a tool that has significant limitations such as time and ambiguities.

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The OneAI Language Studio dramatically reduces development efforts by providing a user-friendly studio that can easily be applied to unstructured data. There are different capabilities that can be applied in the studio such as:

Summarizes: summarizes text while retaining the main idea and important information.
Highlights: Detects key sentences and essential points
Proofreading: filters errors and removes filler words (typical for audio transcription)
Action-Items: this generates a list of tasks needed
Transcribe Audio: creates a transcription for audio
Emotions: Detects emotions such as Happiness, Sadness, and Anger
Names: identify important names in articles and conversation
Anonymize: removes personally identifiable to protect anonymity

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Sample Application: Audio Transcription

For today’s tutorial, we will be using the OneAI Studio for audio transcription. The goal of the project is to extract a transcript from an audio file. Let’s head over to OneAI Studio:

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First, we’ll download our audio. We’ll be using this youtube video for today’s example. After copying the link head over to an online video converter website, which will convert the video to an MP3 file.

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Drag the MP3 audio file to the part of the screen that says ‘Upload File.’ Luckily for us, OneAI has already suggested the ‘Transcribe Audio’ skill automatically.

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Then click, ‘Run Pipeline,’ and view the results:

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Natural Language Processing (NLP) One AI Studio

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Running the Pipeline in Code

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The OneAI studio also generates the code for the selected skill or skills. Copy the audio transcription code that was generated in the previous step.

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To run the code in the local environment, first install the OneAI SDK package.

```

pip install oneai

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import oneai
import requests
import time
import json

with open("<PATH_TO_FILE>", "rb") as f:
text = f.read()

api_key = "YOUR_API_KEY"
url = "https://api.oneai.com/api/v0/pipeline/async/file?pipeline=%7B%22input_type%22%3A%22conversation%22%2C%22steps%22%3A%5B%7B%22skill%22%3A%22transcribe%22%2C%22params%22%3A%7B%22speaker_detection%22%3Atrue%7D%7D%5D%2C%22content_type%22%3A%22audio%2Fwav%22%7D"
headers = {"api-key": api_key, "content-type": "application/json"}

r = requests.post(url, text, headers=headers)
data = r.json()
get_url = f"https://api.oneai.com/api/v0/pipeline/async/tasks/{data['task_id']}"

while True:
r = requests.get(get_url, headers=headers)
response = r.json()

if response["status"] != "RUNNING":
break

time.sleep(5)

json_str=json.dumps(response)

with open('json_data.json', 'w', newline='') as outfile:
outfile.write(json_str)

```

Sample output in JSON file.

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Summary

NLP applications are apparent in our everyday lives and can help businesses both in their customer engagement and the value extraction from the text, audio, and video data they collect. NLP requires specialized skills in the fields of AI and machine learning which can prevent many development teams who lack the time, resources and budget to add NLP capabilities into their application.

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The OneAI NLP Studio gives developers the ability to integrate NLP features into their applications in the most reliable, quick and efficient way possible. Check out the OneAI Language Studio for yourself and see how easy the implementation of NLP capabilities can be.

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