Statistical NLP is the automatic manipulation of natural languages that provides machines with the ability to read, understand, and derive meaning from human languages. Let’s learn about the foundations of statistical nlp and how NLP changes many industries for the better.
Motivations for Statistical NLP
- Natural language processing with r breaks the language into shorter pieces; shorter pieces are easier to derive the meaning from and explore how they work together
- Categorization of content to summarize the meaning of the words
- Applying advanced analytics like optimization and forecasting to the text
- Extraction of structured information from text-based sources
- Analysis of the mood of a large amount of text
- Conversion of voice commands into text and vice versa
Take a look at the following NLP use cases:
- Amazon Comprehend Medical used NLP to extract disease conditions, medications, and treatment outcomes from patient notes, clinical trial reports, and other electronic health records.
- With the help of sentiment analysis, companies can classify incoming customer conversations about the brand on social media, and detect their intentions, desires, and choices.
- NLP is even used for developing a personal assistant that can learn everything about you and then remind you of anything you may forget, like the place where you left your sunglasses or the name of the person you’ve just talked with.
“Human memory is not the same as computer memory. We don’t have pointers. We don’t have addresses where we can just look up the data we need.”
— James Kozloski, an inventor at IBM who focuses on computational and applied neuroscience
- NLP helps media companies detect fake news and decide on the credibility of this or that source
- Amazon’s Alexa and Apple’s Siri use NLP to react to vocal prompts to do everything we ask them to do
- Financial traders use NLP to track news, reports, and comments about possible mergers between companies
“Banks will be able to use NLP to develop a "close to a real-time understanding of what their full portfolio of positions is. It will greatly improve risk management and will be good for society by reducing surprises. NLP will reduce the risk of future financial crises.”
— Elkan, a machine learning specialist in the finance industry
- NLP is also used in the recruiting industry to spot specific desired skills and analyze the candidate's speech, pronunciation, vocabulary, and emotions to tell if the applicant is interested in the position
Statistical Modeling in NLP
Statistical nlp is the process of predicting the next word in the sequence given the words that precede it. Statistical modeling helps to:
- Suggest auto-completes
- Recognize handwriting with lexical acquisition even if it’s in a poorly written text
- Detect and correct spelling errors
- Recognize speech
- Recognize multi-token named entities
- Caption images
- Summarize texts
- Detect primitive acoustic features
- Perform text categorization
Statistical models are used in NLP for two reasons:
- To make algorithms for processing language able to learn from observations of language (and other contextual clues). This is called machine learning. There’s also an alternative called expert systems. However, it doesn’t scale because it is not feasible for engineers to write down all of the “rules” for “understanding” text.
- Natural language is based on referential and prototypical context to disambiguate its precise meaning. In comparison with rule-based systems, statistical models are best for this type of situation-dependent interface and corpus-based work.
Here are the general applications of the statistical models and fundamentals of statistical natural language processing:
- Spatial models: A co-variation of properties within geographic space.
- Spatial models: A co-variation of properties within geographic space.
- Time-series: Frequency-domain methods and time-domain methods.
- Survival analysis: Analysis of the expected duration of time until one or more events happen, such as a death in biological organisms and failure in mechanical systems.
- Market segmentation: Dividing a broad market into the groups of customers with similar characteristics
- Recommendation systems: The filtering system predicts the 'rating' or 'preference' that a user would give to an item.
- Association Rule Learning: A method for discovering interesting relations between variables in large databases.
- Attribution Modeling: A rule that determines how credit for sales and conversions is assigned to touchpoints in conversion paths.
- Scoring: Statistics processing to predict the outcome and assign it a corresponding score.
Simple Code Example of NLP With Python
Python provides many ready-to-use libraries for Natural Language Processing: Natural Language Toolkit used for recommendation systems, sentiment analysis, and building chatbots; Spacy used for autocomplete and autocorrect, analyzing reviews, and summarization; Gensim used for converting documents to vectors, finding text similarity, and text summarization; and Pattern for spelling correction, search engine optimization, and sentiment analysis with Markov model methodology.
Take a look at this code example of natural language processing with python on Indeed.com’s job postings. One of the tasks was to tag the job descriptions with the help of POS tagging and quantization.
According to Wikipedia, POS tagging is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context, i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph. A simplified form of this is commonly taught to school-age children to identify words such as nouns, verbs, adjectives, adverbs, etc. The following code was used to stem and lowercase words.
If you want to learn how to understand and implement Natural Language Processing (with codes in Python), we recommend reading this guide.
“NLP software development services help businesses monitor customer data, respond to customer demands, and improve customer experience in real-time. With the help of NLP, you can get deep insights about your customers and use these insights to grow your revenue and stay on top of your competitor.”
— Vlad Medvedovsky, Founder and CEO at Proxet (ex - Rails Reactor), a software development solutions company
Real-world Examples of NLP in our Everyday Lives
Natural Language Processing technology is used daily, but people often take it for granted and stop seeking more ways to make our lives easier. Let’s review the most prominent examples of NLP in our daily life.
Spell check is one example of NLP that makes the lives of both users and agents easier. Many companies add a spell check to their online forms to avoid miscommunication and easily interpret all incoming messages. Another example of NLP is the autocomplete integrated into search engines to help people find the information they need faster and easier.
Onsite search can also be upgraded with smart search, another NLP technology that studies how users interact with the website, automatically adds contextually relevant synonyms, provides personalized search, and offers products people recently interacted with. One example of smart search for e-commerce is Klevu AI, an eCommerce search solution that delivers search results based on user intention and behavior in real-time.
Have you ever said “Hey Siri” or ‘Ok, Google”? Then you’ve used a digital assistant. And that digital assistant has used NLP to “understand” you. The process involves voice recognition mechanisms. After that, the input is processed via NLP algorithms to obtain its meaning and return a useful response. As NLP models get smarter, we can expect digital assistants like Alexa and Siri to not only help with our daily queries but to also respond humorously or answer questions about themselves. Machine learning will help these assistants provide more personal answers and change the way we communicate with AI-powered systems.
With the help of NLP, messenger bots can advertise a product or service and also interact with the user and provide a personalized communication experience. For instance, NLP chatbot platforms used by Intelliticks, an AI-powered live chat that uses the combined intelligence of AI + Human to create personalized conversational experiences and convert visitors into qualified leads. It uses natural language processing (NLP) to instantly answer repeated customer queries without involving a human operator.
Natural Language Processing Statistics and Trends
The global statistical natural language processing market size is projected to grow from $1 billion in 2020 to $3.7 billion by 2027, at an annual growth rate of 20.1% from 2021 to 2027. More and more companies use NLP, and 53% of leaders said their NLP budget in 2020 was at least 10% higher than in 2019. In 2021, NLP is improving due to the following trends:
- Transfer learning where a model is trained for one task and repurposed for a second task related to the main task.
- Low code tools that allow non-technical users to perform NLP tasks that were once only accessible to data scientists and developers.
- Pre-trained multilingual models that may perform even better than monolingual models.
- Automated customer service to help companies eliminate tedious tasks such as sorting customer support tickets and handling customer queries.
If you want to enhance the opportunities of your business with the help of NLP technology, talk with us at Proxet. Sixty percent of our projects have AI/ML components, including everything from chatbot implementation to image recognition and sentiment analysis. We’ll guide you from the concept to its implementation and help you bring your business to a new level.
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