Natural Language Processing (NLP) is one of the many sub-branches of artificial intelligence (AI). The technology is concerned with the human language interpretation by machines and is used to create intelligent machines that are able to understand and analyze human speech and text.
Each day millions of conversations are made on the social media platform. This is massive data being generated each day voluntarily by users. Add to this the social media surveys, feedbacks, reviews, customer feedback, and so on, itâs no wonder that businesses today have in their hand a massive volume of unstructured data.
However, to be able to squeeze any benefit out of this text-heavy and unstructured data, businesses need to have efficient technology to analyze and structure this data.
Thatâs where Natural Language Processing (NLP) comes into playâŠ
Here in this blog, we will be looking at everything you need to know about NLP and how it works. We will also be looking at some amazing NLP technologies available in the market and their widespread application in various sectors.
What Is Natural Language Processing?
Natural Language Processing (NLP) is a sub-branch of artificial intelligence (AI). Unlike some of the other sub-branches of artificial intelligence, NLP is concerned with the machine interpretation of human text and speech. The technology use algorithms to understand, text or speech and perform analysis to track the context of the speech/text. This helps machines takeover various tedious and repetitive tasks including summarization, machine translation, and others. The ability of NLP to analyze the context of the speech/text, also enables machines to understand the sentiments and detect emotions.
How Does Natural Language Processing Work?
Human speech is diverse, complex, and ambiguous. At present, there are over 6,500 languages in the world with each of the languages carrying its own semantic rules and syntactic. So, the answer to âhow does natural language processing work?â is definitely not simple as well.
To be able to understand such complex and diversified data, NLP uses two processing states. The first state is called the data pre-processing state where the AI algorithms help machines to understand the natural language by transforming the data (speech/text) into the machine-interpretable mode. The second state is Building the NLP Algorithms, which involves the training of the algorithms on the data so it can analyze and interpret the language, as well as, perform specific tasks.
How Much Does Natural Language Processing Work Cost?
Before we move into the details of how much does natural language processing work costs, itâs better to understand the two available modes of integration NLP into your requirements. Itâs what we can call a âbuildâ or âbuyâ mode.
In the âbuildâ or âbuyâ approach, you would have to choose between:
- Customizing an NLP platform
- Licensing NLP cloud API
- Building NLP from open source
Before we get into the detailed costs of each of the models, itâs important to note that each of the three modes comes with unique strengths and limitations. For once, the Open Source NLP models are getting better than ever, with robust features, and functionalities. Simultaneously, the NLP Cloud APIs are also becoming easier to access. Lastly, there are dozens of free NLP toolkits available for users to choose and customize as per their business needs.
Now, letâs quickly take a look at how much does natural language processing work cost:
Building from open source NLP libraries
Following are the average estimated costs of building NLP libraries from open source:
- System cost: USD 0 (Open Source)
- Development Cost: USD 80,000+ (you will have to hire an NLP expert with other developers)
- Timeline: Weeks
- System Capabilities: Limited (it will be mostly restricted to your business needs without any additional features/work)
Licensing an API or NLP platform
Following are the average estimated costs of licensing an API or NLP platform:
- System Cost: USD 10,000 â YSD 40,000 (basic cloud analytics to customizable NLP platform)
- Development Cost: USD 0 (will be handled by the vendor)
- Timeline: Days
- System Capabilities: Customized to your needs (deep and broad)
How Does Natural Language Processing Work in 2 States?
As mentioned earlier, to be able to cope with the complex task of understanding, and analyzing human speech and text, NLP works in 2 states including; data preprocessing and Building NLP Algorithms.
Data Pre-Processing
The first state in NLP work includes the transformation of data into the machine-interpretable mode. The key to NLP lies with its ability to understand and identify the grammatical structure, and semantic of the language, as well as, to relate words with each other to understand the context of the text/speech. This complex processing is achieved by transforming (cleaning) the dataset and making it more organized. This state includes;
- Tokenization:
Tokenization involves breaking of text/speech into single clauses or small semantics
- Part-of-speech-tagging:
This involves the categorization of words into nouns, pronouns, adjectives, adverbs, etc.
- Stemming and Lemmatization
This involves standardization of words into their root forms
- Stop word removal
Stop word removal involves filtering of commonly used words which adds little to no information to the text. This mostly includes articles, prepositions, and others.
Natural Language Processing Algorithms
Once the data transformation (cleaning) is completed, the next step in Natural Language Processing involves building an NLP algorithm. This state includes training of the NLP algorithm so as to make it efficient in performing specific tasks and interpreting human language.
Two main algorithms used in this state include;
– A rule-based approach
This approach heavily relies on the grammatical rules hand-crafted by linguists, knowledge engineers, and grammar experts. This approach was the pioneering approach in the development of NLP algorithms
– Machine learning approach
Machine learning is the latest and advanced approach to build NLP algorithms. This approach uses statistical methods to enable algorithms to learn, and interpret human language, as well as, perform specific tasks.
How does natural language processing work in converse smartly?
Converse Smartly is an in-house NLP project of Folio3. The Converse Smartly software is one of the most advance and powerful NLP platforms that converts audio to text. The platform is used by organizations, and individuals alike to improve their work efficiency, and accuracy. The application can be used with equal efficiency to analyze speech even with multiple speakers like during meetings, conferences, seminars, and others.
The software engages advanced speech recognition technology to deliver the most precise results, at accuracy levels that are unmatched by any other speech recognition software available on market. To improve the user experience, the tool also includes various build-in features, and functionalities that assist in further enhancing the productivity, and efficiency of users.
The massive success of Converse Smartly established the dominance of Folio3 as a reliable, and leading tech company focused on the development of advanced technology applications including machine learning, and NLP.
The platform leverages powerful technologies including Microsoft Azure, IBM Watson, Amazon AWS, and Google Speech-to-Text API, as well as, Pythonâs NLP toolkit to deliver exceptional accuracy, and an interactive user interface.
The cutting-edge technology in Converse Smartly enables businesses to improve transcribing speed, resulting in faster, more accurate, and efficient operations for businesses. The platform can be used with equal efficiency for iOS, Windows, and Android devices. The only requirement for Converse Smartly is a stable internet connection.
Features offered by Converse Smartly
Some of the amazing features offered by Converse Smartly include;
- Multi-language support including; English, German, and Spanish
- Analyzing capabilities for speech and voice patterns
- Text analytics feature
- High accuracy with low-quality audio
- Speech recognition technology to understand the context of speech
- Multi-speakers detection
- Sentiment analysis
- Powerful algorithms to identify topics, and keywords
- Automatic speech summary
How does Google natural language processing technology work for a startup, SMEs, and Enterprises?
Google natural language processing technology is undoubtedly one of the most advanced and mature speech recognition technology that is widely used across the world. Some features of the technology that greatly help in the sustainability, and growth of startups, SMEs, and enterprises include;
Speech Recognition
The capability of Googleâs speech-to-text service is unprecedented in the market. The platform offers an easy-to-use API for the conversion of audio to text. The Google speech-to-text API is able to recognize as many as 120 languages, including various variants. The technology also offers real-time streaming, call center audio transcription, recording audio processing, and other features.
Turn Text to speech
Google natural language processing technology can efficiently transform the written text into speech. Again, the accuracy and efficiency of the tool are comparable to the best technologies available in the market. The text to speech conversion by Googleâs natural language processing is both grammatically and contextually correct, along with options for natural voice selection.
Language Identifier
Google natural language processing can understand as many as 120 languages and variants. This means that itâs easier than ever for users to use Googleâs technology to convert speech to text, or text to speech. Additional features like voice searches and commands further improve the user experience of the platform.
Audio Transcriber
The Google Speech to Text software offers an active noise cancellation service for transcribing. The system is also able to account for the context of the speech, as well as, for language and proper nouns.
Video Subtitling
The Google Speech to Text platform is also actively used in video subtitling. The platforms through its advanced machine learning models make it easy and streamline to extract subtitles from the video or audio file.
Google Speech-to-text Service Features
– Automatic Speech Recognition
Googleâs automatic speech recognition (ASR) module is powered by a robust neural network that enables the module to be used accurately in speech transcription and voice searches.
– Global Vocabulary & Punctuation
Google speech recognition platform is one of the most comprehensive and advanced tools with the vastest of the database in the world. At present, the platform supports a total of 120 languages and variants, which is the most supported by any platform. The massive database, robust artificial intelligence, and machine learning algorithms also make the platform one of the most accurate and comprehensive transcription systems available in the market.
– Noise management
Googleâs active noise cancellation feature enables users to avoid time-consuming tasks of removing background noise. Googleâs cloud-based API comes equipped with the features to automatically extract the key information from an audio file, and ignore/suppress the background noise in the environment.
– Streaming recognition
Streaming recognition is another advanced feature of Googleâs speech-to-text software that enables businesses and individuals alike to have transcription services in real-time as the person speaks. Thus saving much time and improving operational efficiencies.
– Content filtering
Another advanced feature of Googleâs speech-to-text software includes content filtering for profane or inappropriate content. This means that the system automatically deletes filter any profanity or inappropriate content from the transcribed results.
– Integrated APIs
Google speech to text software enables users to directly upload the audio/video files into Googleâs cloud storage, leveraging the full potential of Google Cloud Platform (GCP). This means that users wonât have to store large media files in their storage devices.
How Does Amazon Transcribe Natural Language Processing Technology Work for a startup, SMEs, and Enterprises?
Amazon Transcribe natural language processing technology also known as the AWS Speech Recognition, is another highly advance and widely used transcribing technology. The AWS speech recognition from Amazon is widely used in the industry for beefing up operational efficiency and making timely smart decisions.
The Amazon transcribe natural language processing technology is a dynamic system that is equally suited for individual and business use. Some of the features of the platform include;
– Improving Customer Service
Amazon Transcribe enables users to develop robust applications for better customer facilitation. The Amazon transcription platform may be used to convert all audio/video files into text, whereas, it takes the transcription to another level by enabling search and analysis function for enhanced data mining. The platform can be used to extract insightful data through calls transcribing, resulting in better customer servicing.
– Captioning & Subtitling
Another widely used application of Amazon Transcribe is for the creation of captions and subtitles. The platform lets content creators automatically create captions and subtitles for videos and audio files. This greatly improves the accessibility and ranking for the content, giving content creators a better chance to reach a larger audience. Also, by integrating Amazon Translate with Amazon Transcribe, content creators have a great chance to seamlessly localize the content for a specific region.
– Cataloguing Audio Archives
Last but not least, the AWS platform also enables users to easily manage the archives of all the audio/video files. Users can transcribe all audio/video files, before effortlessly archiving the files into searchable formats. The platform also enables users to check for controversial content, as well as, any required compliance check quickly.
How does IBM Watson’s natural language processing technology work for a startup, SMEs, and Enterprises?
IBM Watson Natural Language Processing Technology brings in various powerful and advanced tools for SMEs, and enterprises to optimize workflow processing for increased efficiency and productivity. Some of the features of IBM Watson Natural Language Processing technology include;
– Speech to Text Conversion
IBM Watson’s speech-to-text conversion feature enables businesses of all sizes and nature to get easily convert audio/video files into text. The platform supports a wide range of languages, voices, and dialects, as well as, has the feature to understand the context of the content, for better accuracy.
– Chat Bots
Apart from being used in the speech-to-text use case, the IBM Watson is also widely used to integrate chatbots on websites. This greatly improves the customer service for online businesses, as they can offer 24/7 customer support through automated chatbots.
– Language Translation
IBM Watson technology also offers language translation for startups, SMEs, and enterprises. The technology also enables businesses to integrate multilingual chatbots on the websites, thereby, massively improving their chances to reach and connect with customers from different regions of the world.
– Visual Recognition
IBM Watson is also being used for auto-tagging of images. By using machine learning and computer vision technology, the AI algorithms in IBM Watson can be trained for visual content tagging.
How does Azure cognitive natural language processing technology work for a startup, SMEs, and Enterprises?
Azure Cognitive natural language processing technology brings in the powerful combination of artificial intelligence, and machine learning to enable Startups, SMEs, and Enterprises in analyzing emotions, sentiments, and visual objects. Itâs also one of the highly developed technology for speech conversion purposes.
Some of the wide applications of the technology include;
- Azure Bot Service
- Azure Databricks
- Azure Cognitive Search
- Bing Auto Suggest
- Bing Custom Search
- Bing Entity Search
- Bing Image Search
- Bing News Search
- Bing Spell Check
- Bing Video Search
- Bing Visual Search
- Bing Web Search
- Cognitive Services
- Computer Vision
- Content Moderator
- Custom Vision
- Data Science Virtual Machines
- Face Recognition
- Azure Machine Learning
- Machine Learning Studio
- Microsoft Genomics
- Translator Speech
- Language Understanding
- Form Recognizer
- Ink Recognizer
- Personalizer
- QnA Maker
- Speaker Recognition
- Speech Translation
- Speech to Text
- Text Analytics
- Text to Speech
- Translator Text
- Video Indexer
- Kinect DK
- Anomaly Detector
- Azure Open Datasets
- Immersive Reader
Final Words
Natural Language Processing has come up as one most widely developing and high-utility fields of artificial intelligence. Technology has already made in-roads into various fields and applications used in our personal and professional lives.
The NLP technology brings in the automation required by businesses of all sizes to streamline their workflow processes, as well as, access the actionable insights from the massive unstructured data. This subsequently enables businesses to design better services, and deliver enhanced customer services. While it is a complex technology, the availability of online NLP makes it easy for businesses to integrate the technology into their various business functions for improved efficiency, and productivity.
