What is Natural Language Processing (NLP)? It is an interdisciplinary field, connecting computer science, artificial intelligence, and linguistics. The goal of NLP is to make machines “understand” natural languages.
Various NLP strategies are designed to meet several business and scientific needs, from translation and question answering (think of Siri or Alexa) to identification and classification of leads (here's a great explanation by a Forbes columnist).
So, what are some common applications of NLP modeling?
- Sentiment Analysis.
- Chatbots and Digital Assistants.
- Machine Translation.
- Text Summarization.
- Market Intelligence.
- Auto-Correct and Input Prediction Functions.
- Text Classification.
- Text Extraction.
NLP Basis: How this Technology Works
The principal goal of NLP is converting human speech (either written or spoken) into “machine language,” that is, data that is acceptable and understandable by computers for further processing.
Machines do not understand human language the way we do, at least not yet. Nevertheless, due to advancements in programming and computer processing, they can already do a lot. Although NLP is limited to certain areas right now, in those areas, NLP works like magic. NLP modeling and meta-programs can save lots of time.
NLP Basis: How this Technology Works
NLP modeling step by step is easily accessible via several open-source Python libraries like spaCy, NLTK, and TextBlob. For complex, business-oriented projects it is advisable to find custom software development solutions in the market.
Working with elements in NLP modeling is similar to working with other business tasks. That is, one needs to build a pipeline. You may get an idea for a project from your marketing or sales experience, then break the project up into very small subtasks and work on them individually. Modeling NLP processes looks the same: divide the problem into little chunks and then use machine learning to solve each chunk, one step at a time. Then, by connecting NLP modeling components that feed into each other, several complex things become possible.
In sum, NLP strategies are built on using computers to “read” text by simulating the human ability to do so.
NLP Modeling Components
In this digital era, messaging apps accompany us throughout our daily routines. Messaging app usage has already surpassed social networks. As a contributor to Towards Data Science puts it, “The consumption of messaging platforms is further expected to grow significantly in the coming years; hence this is a huge opportunity for different businesses to gain attention where people are actively engaged.”
To give you a better picture of natural language processing technology and its applications, here's a brief overview of the key NLP modeling components.
Thus, the architecture of an NLP pipeline can include several blocks:
- a user interface;
- some NLP models (depending on the goal of a system);
- a module for Natural Language Understanding (this one should “grasp” the meaning of words and sentences);
- a preprocessing block;
- various microservices that connect the components;
- an infrastructure that contains the complete solution.
It would be unwise to do NLP modeling step by step from scratch each time, so typically NLP software contains reusable blocks. For tailor-made solutions, it is typically the Natural Language Understanding (NLU) module that requires substantial adjustments. No wonder, since the NLU block deals with matching proper corresponding input tokens (like words, phrases, and their combinations up to large documents) to their meanings.
Techniques for Basic NLP Strategies
Natural Language Processing has developed into a diverse sphere, using a variety of models, methodologies, and instruments. Let’s have a look at the most common NLP techniques:
- Text Embeddings are the NLP basis. NLP algorithms process words as symbols, represented by one-hot vectors. The idea is that each word is “explained” by the words that appear close by most frequently. Thus, those with similar meaning should have similar vectors. Among the most popular instruments for embedding are Word2vec by Google and GloVe by Stanford.
- Machine Translation is a very complicated task, involving both analyzing and generating language. It has huge commercial applications. ResearchAndMarkets.com values the machine translation market at $550 million in 2019 and projects the market to exceed $1 billion by 2025. The most widely-known applications are Google Translate and Facebook, which uses this technology to translate posts and comments automatically.
- Dialogue and Conversation capabilities provided by NLP are used in chatbots and personal assistants like Alexa, Siri, M, Google Assistant, and Cortana. Chatbot funnels may significantly increase customer acquisition, retention, and customer loyalty.
“Chatbots are becoming increasingly more popular with customers and organizations. Custom software development solutions are required to satisfy that demand. Almost half of the CIOs surveyed by Gartner in 2020 reported that they planned to invest in or had already invested in AI-based chatbots.”
— Vlad Medvedovsky at Proxet, custom software development solutions company.
- Sentiment Analysis is the backbone of brand image monitoring. Social media has become an important source of data for businesses struggling to anticipate the needs of their customers. With almost 4 billion social media users globally, content—texts, images, audios, videos—grows exponentially, making manual sentiment analysis virtually impossible.
“Considering sentiment analysis utilizes social media, mobile apps, websites and forums to collect this priceless data, it becomes a veritable mine for organizations based on which they can improve their products, services, brand reputation and more to become market leaders”
— Pradeep Govindasamy, CTO at Cigniti, software testing company.
- Question Answering systems are designed to extract information from documents, conversations, online searches, etc. They save time and energy for a user, who gets a short and concise answer in no time without having to sift through large volumes of data (such as long documents or transcripts). Businesses can use QA systems for internal and external users to smooth out work routines and customer experiences.
- Text Summarization is designed to get the gist of large publications. As push notifications and article digests become more and more popular, the value of quickly generating intelligent and accurate summaries is growing. There are two fundamental approaches. Extractive summarization uses words and phrases from the original text to create a summary. Abstractive summarization is more complex, and requires an algorithm to “learn” an internal language representation, but with the benefit of providing more human-like responses, paraphrasing the essence of the original text.
NLP Modeling Step-by-Step: How Can Computers “Understand” Human Language?
Natural Language Processing uses a computer to disassemble text and speech into their smallest elements and look for connections between them. That is a complicated task; many of the operations humans learn to perform with little conscious thought while reading, writing, or speaking are challenging for an algorithm. Thus, pre-processing texts is one of the most basic elements in NLP modeling. Usually, it includes:
Sentence Segmentation
The text is split into separate sentences. Sometimes, punctuation marks provide a reliable guide. Thankfully, there are advanced techniques that work when a document isn’t formatted clearly.
Word Tokenization
Each sentence is broken into separate words or “tokens.” For texts, space is usually enough to denote a new “token.” Punctuation marks are also tokenized because they may convey some meaning.
Predicting Parts of Speech for Each Token
To decipher the meaning of a sentence, we have to understand what it is made of. The part-of-speech classification model, trained using millions of sentences with the part of speech already tagged for each word, is suitable for the task. Let’s use the sentence “London is the capital and most populous city of England and the United Kingdom” as an example:
Text Lemmatization
Finding the most basic form (“lemma”) of each word. For example, “be” is a lemma for “is”:
Identifying Stop Words
Some words appear frequently, but convey little meaning (like “a,” “the,” and “and”). So they are filtered out to avoid muddling consequent analysis. The list of such words may vary depending on the application. In this example, the stop words are grey:
Dependency Parsing is the next step
It means figuring out how the words in each sentence relate to each other. A tree of dependencies is built with the main verb in the sentence as its “root.” Each word is assigned a parent word and the type of relationship between those words can be predicted.
Named Entity Recognition (NER)
Named Entity Recognition (NER) detects and labels nouns as the concepts they represent. NER may detect objects such as dates/times, people’s names, names of the companies, physical or political geographic locations, product names, amounts of money, events, etc.
Coreference Resolution
Taking the context of each sentence into consideration (to link pronouns to the nouns they represent, for example):
Proxet can help any business use the immense potential of modeling NLP, voice technology, and chatbots to their fullest extent. We leverage the expertise of the top experts in the field to help our clients succeed. Besides, our company provides team augmentation services and can find some extraordinary NLP engineers to tackle the most daring projects.