Fundamentals of Natural Language Processing
Natural Language Processing (NLP) is a subset of artificial technology and linguistics that focuses on communication between humans and machines. In NLP, computers are used to process large amounts of natural language data to help machine learning models interpret and respond to text and voice data in a human-like way.
In today’s digital world, the role of NLPs is integral to our online experience as communications become more commonplace across the internet. Websites, search engines, and social media platforms all rely on human language and the need for NLPs to handle the massive amounts of language inputs uploaded or generated online every second. So, what is natural language processing?
Understanding Natural Language Processing
The field of NLP involves making computers understand, interpret, and generate human languages in meaningful ways. Similar to modern education practices, NLP teaches a machine how to interpret language based on context, grammar, and sentiment, not just rote memory of the definitions and meanings of individual words isolated from a sentence.
Due to the complexity of human language, there are many challenges present when training an NLP model:
Ambiguity: Human language has several figures of speech that can make it challenging for a machine to understand the true meaning of a given phrase.
Context: Conversation is a dynamic process that changes over time, requiring machines to form memories of each communicative interaction.
Sarcasm and Irony: Detecting humor and a play on words can be difficult for most humans, and even more so for computers.
Language Diversity: Humans have created hundreds of languages, each containing unique intricacies for machines to learn.
Overview of the NLP Process
NLP can be broken down into a basic set of steps for machines to follow. Each step becomes more complex depending on the purposes and nature of the algorithms being trained.
Text Collection: First, test data must be organized and arranged as learning material before training can occur.
Text Cleaning: Text is then optimized by removing unnecessary punctuations, numbers, or other special characters.
Tokenization: Individual words are then isolated and labeled as tokens for the machine to use.
Stop Word Removal: Excess wording like articles of speech (a, an, the) are removed from the language data set.
Lemmatization/Stemming: Words are reduced to their root form, removing verb tenses and agreements.
Vectorization: Further alteration of the language learning set that converts it to data compatible with machine learning algorithms.
Training: Once data is fully processed, it is then fed to the learning algorithm to begin training.
Evaluation: In the final step, either humans or the machine evaluates output data and adjusts its parameters for improved accuracy.
Advanced components of NLP
While this constitutes the general step-by-step process of Natural Language Processing, there are additional steps that can be applied to more advanced models:
Named Entity Recognition: A process in which the machine learns to identify and classify names, persons, organizations, locations, etc, similar to proper nouns, but can also include times, dates, and percentages.
Part-of-Speech Recognition: Also known as POS tagging, is how a machine identifies the operation of a word (verbs, nouns, adjectives, etc) and how it is used within a sentence.
Syntactic Parsing: Sometimes referred to as Dependency Parsing, this is a process where the machine will identify grammar structures and create a map tree of a given sentence.
Sentiment Analysis: Referred to as opinion mining, sentiment analysis helps machines identify the opinions and emotions present in the given text.
Common NLP Algorithms
Since language is so complex and diverse (with over 7,000 spoken across the world), there is an inherent need for multiple algorithms to help train machines on human language and communication.
Bag of Words (BoW): A BoW algorithm focuses on word frequency in text with no regard for its placement. This means that grammar can be disregarded which helps to improve computational requirements by being more simplistic and scalable.
Term Frequency-Inverse Document Frequency (TF-IDF): This algorithm attempts to identify a term’s frequency across multiple documents, allowing it to score keywords to suggest their importance.
Word2Vec: A combination of algorithms that maps large text datasets in a multi-dimensional spread, assigning words to certain vectors and adjusting their proximity to other words that share similar contexts.
Long Short-Term Memory (LSTM): A recurrent neural network that is trained to recall vast sequences of input information to establish advanced context and understanding of text and speech.
NLP Tools and Libraries
For developers, there are numerous resources available to train NLP models and algorithms. Many libraries and frameworks already exist that offer access to code functions for tokenization, POS Tagging, and more. Some of the most popular libraries include NLTK, Gensim, SpaCy, Stanford NLP, and OpenNLP.
Python tends to be the most common programming language used for developing NLPs as well because of its large community and straightforward syntax. Python’s use among statisticians and data scientists also helps with the more interdisciplinary aspects of NLP between computer science and linguistics.
Practical Applications of NLP
After years of research and study, NLPs can be observed almost everywhere in our daily lives. They offer the ability to assist in how humans communicate via speech recognition, text summarization, and language translation.
As these systems become even more advanced, they can also begin to replicate tasks that we generally associate with humans such as customer service and online help chatbots. With innovation in AI increasing at an exponential rate, these aspects of business operations are some of the most likely to defer to artificial intelligence.