Natural Language conversations like speech and written scripts distinguish humanity from other species. They have defined how humans have embarked on the journey of innovations and nourishing intellect. In recent days, leveraging these powerful tools called ‘Writing’ and ‘Speech’ to build Artificial Intelligence solutions has gained significant impetus in the AI development saga. Unlock the potential of data with Natural Language Processing and Machine Learning Insights for cutting-edge solutions and transformative innovations, and propel the next wave of AI advancements.
Conversational AI comprising natural language processing and machine learning has revolutionized how organizations interact with customers, streamline operations, and gain an edge over competitors.
From a business leader’s perspective, conversational AI offers a plethora of tools to accomplish data-driven decisions, anticipate customer needs, and foster brand loyalty. It also provides invaluable insights into customer behavior and preferences empowering the leaders to make informed decisions aligned with their business objectives. This blog explores how Natural Language Processing and Machine Learning are redefining the future of Conversational AI.
NLP and ML: The Building Blocks of Conversational AI
We all use NLP applications in one or the other form in our daily life. Popular virtual assistants embedded in Google, Apple, and Amazon products such as Google Assistant, Siri, and Alexa are NLP applications we often come across.
NLP is a branch of Artificial Intelligence that handles inputs in the form of human conversational language like speech and text. Further, it helps computers comprehend such text and speech-based inputs. NLP applications such as sentiment analysis, speech-to-text transcription, language translation, search autocomplete, voice-operated GPS systems, Customer service chatbots, predictive text software, and email filters have flooded our work environments.
On the other hand, Machine Learning (ML) is also a sub-field of Artificial Intelligence. This discipline helps us build models that can perform complex tasks such as categorizing images, predicting price fluctuations, or analyzing data predominantly carried out only with human intelligence.
NLP and ML must work in tandem to realize Conversational AI applications. NLP helps machines to understand and process human language in a meaningful manner whereas ML supports NLP by allowing the system to learn from data, recognize patterns, and improve its predictions and responses.
Natural Language Processing Tools
Assume you must carry out sentimental analysis on a huge data set. Manually accomplishing this task is time-consuming and non-productive. Natural Language Processing Tools play a vital role in accomplishing such conversational AI tasks. Popular NLP Tools are available as open-source Libraries or pre-built, cloud-based services like SaaS software. Popular open-source NLP tools are:
PyTorch-NLP:
Are you familiar with PyTorch open-source Machine Learning Library? Then, using PyTorch-NLP for your Natural Language Processing needs is recommended.
It offers various utilities, like text classification, text-translation, language modeling, sentiment analysis, named entity recognition, and sequence tagging. Moreover, torchnlp supplements PyTorch by providing access to simple text data processing and image transformation features. In a nutshell, this NLP tool offers robust support in rapid prototyping and text encoding.
NLTK (Natural Language Toolkit)
NLTK or Natural Language Toolkit is a popular tool power-packed with algorithms supporting NLP techniques like classification, stemming, parsing, tokenization, tagging etc. One of the drawbacks of this tool is the library stores all the data as strings, as a result accomplishing complex operations is quite challenging and models built using these tools lack speed. Overall, it is the best tool, to begin with, and provides comprehensive documentation in the form of a book titled “Natural Language Processing with Python.”
SpaCy
SpaCy is a popular open-source NLP library written in Python and is popular amongst practitioners.
It is easy to install with access to proper documentation and can handle huge datasets. It scores well in NLP tasks such as sentence boundary detection, named entity recognition (NER), and dependency parsing. Unlike NLTK it represents data as objects overcoming the limitations of NLTK in handling data with speed. This NLP tool is widely recommended for projects requiring high accuracy and optimized speed. SpaCy is not the best option for models employing extensive deep-learning techniques. Importantly, it works seamlessly with other popular data science frameworks like PyTorch, TensorFlow, and scikit-learn.
OpenNLP
OpenNLP is Apache’s contribution to the NLP realm. It is a popular NLP library, that helps you perform NLP operations such as part-of-speech tagging, coreference resolution, parsing, named entity recognition, chunking, and tokenization. Apache OpenNLP offers more flexibility as it is available as a Command Line Interface, or, as a standalone application. Developers adore it as it is open-source and free to use under the Apache license.
It easily integrates with other Apache software as it is built upon Apache Foundation. This tool provides you with an API of pre-trained and fine-tuned models that can be deployed for specific projects.
Stanford CoreNLP
Stanford Natural Language Processing Community has contributed to Stanford CoreNLP in the arena of NLP. It is a popular Java-based NLP library. Even though primarily built on Java, it offers APIs in other programming languages.
This library supports salient NLP operations like human (natural) language analysis, tokenization, part-of-speech tagging, sentiment analysis, and named entity recognition.
It is available to everyone for free under the GPL license. Also, Stanford offers a commercial version of the product and a unique license intended for use in research and academic purposes.
Natural Language Processing and Machine Learning are revolutionizing digital technologies by enabling machines to understand and interpret human language. NLP facilitates communication between humans and machines, while ML drives data-driven insights and automation, leading to smarter AI solutions and innovations.
How to Choose the Right NLP Tool?
Having witnessed the features of various Natural Language Processing tools, now let us delve into choosing the right tool for your application needs. Predominantly selecting the right NLP tool depends on THREE prominent factors:
Nature of Models: Firstly, verify if the tool chosen offers pre-trained models you need for your project, or do you need to train the models on your own. Identifying this can significantly save you a lot of time during production.
Integration Capabilities: Verify if the NLP tool selected can be integrated easily with another tech stack used in your project. If it doesn’t integrate with your application, no point in using it, no matter how powerful or popular it is.
Language Support: The NLP tool selected should support the language you are working with. Otherwise, you cannot implement features like sentence segmentation, tokenization, and part-of-speech tagging, as they improve the accuracy and effectiveness of your NLP project.
In addition, you must consider factors like scalability, customization, speed, and accuracy, depending on your needs.
Natural Language Processing Techniques
After learning about the NLP tools and the strategies, let us focus on different Natural Language Processing techniques commonly employed in preparing your data to build conversational AI applications. Text data from natural language is unstructured and noisy. It’s important to transform messy text into a usable format for training machine learning models, and improving results and insights.
- Text Preprocessing: This is the first step of NLP where raw text is cleaned and tuned for analysis. It comprises tasks like converting text to lowercase, removing punctuation, and removing whitespaces.
Example:
Original Text: “The T-20 World-Cup 2024 is Hosted by USA and West Indies.”
Pre-processed Text (convert to lowercase): “the t-20 world-cup 2024 is hosted by usa and west indies.”
- Tokenization: Tokenization follows the text preprocessing. It means breaking down text into smaller units called tokens. Such tokens can be words, phrases, or even individual characters. Here is an example of tokenization:
Example:
Consider the sentence: “Artificial Intelligence is a fascinating and challenging stream.”
After tokenization, we get the following tokens:
[‘Artificial’, ‘Intelligence’, ‘is’, ‘a’, ‘fascinating’, ‘and’, ‘challenging’, ‘stream’ “‘]
Each word and punctuation mark acts as a separate token. Such tokens are employed in further processing like parsing, part-of-speech tagging, or feeding into machine learning models to culminate with sentiment analysis or language translation.
- Stemming and Lemmatization: The objective is to extract the ‘ROOT’ or base form of the tokens generated in the previous step.
While stemming is a rudimentary approach that tries to chop off the prefix or suffix of the token to arrive at the root form, Lemmatization is a more sophisticated approach of obtaining the verb form of the token.
Example(s) of Stemming and Lemmatization:
Consider the tokens Sticker, Sticks, Stuck
Stemming works correctly for ‘Sticker’ and ‘Sticks’ by chopping off ‘er’ and ‘s’ but fails to identify the root of stuck. While Lemmatization identifies the verb as ‘Stick’
Similarly, if we apply Lemmatization for the tokens:
‘Changing’, ‘Changes’, ‘Changed’, ‘Changer’
Lemmatization returns ‘Change’ whereas the Stemming returns ‘Chang’
- Part-of-Speech Tagging: In the next step every token is assigned a tag that indicates its part of speech (noun, verb, adjective, etc.). This facilitates understanding the grammatical structure.
Here is a simple example of Part-of-speech tagging:
- Example Sentence: “The dog sat on the chair.”
- Tagged Tokens:
[ (The, 'DT'), (dog, 'NN'), (sat, 'VBD'), (on, 'IN'), (the, 'DT'), (chair, 'NN'), (., '-')]
Note: DT- Determiner; NN- Noun; VBD- Verb; IN- Preposition.
- Named Entity Recognition (NER): This NLP technique recognizes and classifies named entities like people, places, organizations, etc., from the given text.
- Example Sentence: “Mohan Das Gandhi was born in Gujarat, India.”
- Recognized Entities:
[('Mohan Das Gandhi', 'PERSON'), ('Gujarat', 'LOCATION'), ('India', 'LOCATION')]
- Dependency Parsing: This step helps to interpret the grammatical structure of a sentence. It establishes relationships between “head” words and words that modify those heads.
- Example Sentence: “The quick brown fox jumps over the lazy dog.”
- Parsed Structure:
[('jumps', 'ROOT'), ('fox', 'nsubj'), ('quick', 'amod'), ('brown', 'amod'), ('over', 'prep'), ('dog', 'pobj'), ('the', 'det'), ('lazy', 'amod')]
Note:
- ‘nsubj’: stands for the nominal subject.
- ‘ROOT’: stands for the central verb.
- amod: stands for the adjectival modifier.
- pobj: stands for the prepositional object.
- det: stands for the determiner.
- Sentiment Analysis: This is a process to determine the emotional traits behind a series of words, for gaining an understanding of the attitudes, opinions, and emotions expressed in the online platforms. Usually, this analysis is carried out to determine customer mood in online reviews.
- Positive Sentiment: “I had an amazing experience shopping on your website! Definitely, I will shop here again!”
- Neutral Sentiment: “The checkout process was straightforward, and the delivery time was as expected.”
- Negative Sentiment: “I am disappointed with my recent purchase. The website was clunky and kept crashing.”
- Coreference Resolution: Identifying when different words refer to the same entity in a text is critical to understanding the context and reducing ambiguity.
- Example Text: “Raj said he would buy a car. The car is expensive.”
- Resolved Coreferences: “Raj said Raj would buy a car. The car is expensive.”
- Natural Language Understanding (NLU): This involves machine reading comprehension, where the intent and meaning behind the text are interpreted.
- Example Input: “Book a flight to Delhi.”
- System Understanding: ‘{“action”: “book_flight”, “destination”: “Delhi” }`
- Natural Language Generation (NLG): The final step where the system generates clear and contextually relevant text, often in response to user input.
- Example Input: ‘{“greeting”: “Hey”, “name”: “Paly”, “time_of_day”: “evening”}`
- Generated Text: “Hey Paly, good evening!”
Natural Language Processing Examples or Case Studies
Organizations can benefit from NLP in a wide range of methods. Concrete examples of NLP are:
- NLP in Building Generative AI Applications
- NLP for Customer Experience Analytics
- NLP for Recruitment
- NLP for Customer Service
NLP has transformed the way Generative AI is used in real-world applications. Gen AI tools like ChatGPT use unsupervised learning algorithms and can generate content without being trained on what is the correct response. Applications like ChatGPT are revolutionizing fields like content writing, text summarization, translation, and customer service.
Similarly, NLP for Customer Experience Analytics helps your organization to capture the sentiments of customers about your brand, understand their feelings towards your product or service, get insights on which new products or services customers are eager to buy, get insights on which products you must stop producing and which ones to be scaled up, and finally gain insights on how to boost customer satisfaction.
NLP for Customer Service is a vital application that enables customers to be fully empowered to access their requirements 24/7 as manual method of customer service is limited concerning bandwidth and availability. A completely automated NLP-driven customer service keeps your customers delighted.
NLP for HR and Recruiting staff ensures your internal stakeholders like employees and potential employees seeking a position in your organization are served in a better manner. Also, HR managers can swiftly recruit the best talent available through NLP-driven tools.
Analysis of ROI and operational efficiency gains through Natural Language Processing and Machine Learning based applications is another benefit for leaders and executives:
NLP helps leaders determine which metrics are most relevant to their business goals. As a leader, you can integrate Natural Language Processing and Machine Learning with business processes to improve decision-making and enhance the performance of workflows.
Your organization can leverage the benefits of predictive analytics to analyze historical data and predict future trends through NLP-based applications. This also helps in pre-emptive problem-solving and optimal resource allocation.
You can deploy NLP-driven automation for routine tasks like data entry and report generation and utilize human intelligence for more complex tasks.
These pointers provide a bird’s eye view of how organizations can harness the power of NLP to drive significant improvements in operational efficiency and ROI.
The Future Landscape of Natural language Processing and Machine Learning
The future landscape of NLP, ML, and conversational AI is looking fascinating:
- Advancements in NLP, ML, and Conversational AI take industries to more sophisticated and nuanced human-computer interactions. The future AI can understand context and subtext, manage more complex conversations, and offer personalized experiences.
- Real-time language translation, sentiment analysis, and automated content creation will be common tools for global enterprises.
- Executives must stay informed about the latest research, partnering with tech firms, and investing in upskilling and talent development will be vital.
- Significant R&D investments will drive innovation in conversational AI, leading to more intuitive and intelligent virtual assistants. Such initiatives offer a competitive edge to early adopters.
Conclusion
To conclude, this blog explored various NLP tools and techniques in depth and emphasized how Natural Language Processing and Machine Learning can help organizations leverage the benefits of Conversational AI to build systems that learn from data, recognize patterns, and improve predictions and responses to help organizations be more productive and intuitive. The blog also provides common NLP applications employed to enhance performance with better ROI.
