Klu raises $1.7M to empower AI Teams  

Natural language processing (NLP)?

by Stephen M. Walker II, Co-Founder / CEO

What is natural language processing (NLP)?

Natural Language Processing (NLP) is an interdisciplinary subfield of computer science and linguistics, primarily concerned with enabling computers to understand and manipulate human language. It involves processing natural language datasets, such as text or speech corpora, using either rule-based or probabilistic machine learning approaches, including statistical and neural network-based methods.

The ultimate goal of NLP is to create a computer system capable of "understanding" the contents of documents, including the contextual nuances of the language within them. This understanding allows the system to accurately extract information and insights contained in the documents, as well as categorize and organize the documents themselves.

NLP has a wide range of applications. For instance, it powers the recognition and prediction of diseases based on electronic health records and patient’s own speech, filters and classifies emails to prevent spam, helps identify fake news, tracks news and reports for financial trading, and aids in talent recruitment. It also plays a vital role in the operation of chatbots and virtual assistants, sentiment analysis, text classification, text extraction, and summarization.

The process of understanding and manipulating language is complex due to the inherent ambiguities and nuances in human language. This complexity often requires the use of different techniques to handle different challenges before integrating everything together. Some of these techniques include tokenization, stemming, lemmatization, and topic modeling. Programming languages like Python are commonly used to perform these techniques, with libraries such as the Natural Language Toolkit (NLTK) providing a wide range of tools for tackling specific NLP tasks.

NLP has made significant strides over the years, transitioning from hand-coded, rules-based systems to statistical NLP, which combines computer algorithms with machine learning and deep learning models. Today's NLP systems can "learn" as they work, extracting increasingly accurate meaning from vast volumes of raw, unstructured, and unlabeled text and voice data.

What are some common NLP tasks?

Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables machines to understand, analyze, and interpret human language. It combines the power of linguistics and computer science to study the rules and structure of language, and create intelligent systems capable of understanding, analyzing, and extracting meaning from text. Here are some common tasks in NLP:

  1. Tokenization: This is the process of breaking down text into smaller units called tokens. For example, a sentence can be tokenized into individual words.

  2. Part-of-speech tagging: This involves identifying the grammatical category of each word in a sentence, such as whether a word is a noun, verb, adjective, etc.

  3. Dependency Parsing and Constituency Parsing: These tasks involve analyzing the grammatical structure of a sentence, identifying the relationships between words, and organizing the words into a parse tree or syntactic tree.

  4. Lemmatization and Stemming: These techniques reduce words to their base or root form. For example, the words "running", "runs", and "ran" would all be reduced to the base form "run".

  5. Named Entity Recognition (NER): This task involves identifying and categorizing named entities in text into predefined categories such as person names, organizations, locations, etc.

  6. Text Classification: This task assigns a label or a category to a piece of text based on its content or context. For example, it can be used to filter spam emails, detect sentiment in customer reviews, or classify documents by genre or author.

  7. Text Summarization: This involves creating a concise and coherent summary of a longer text, while preserving its main points and information.

  8. Text Generation: This task involves producing natural language text from some input, such as keywords, images, or other texts. It can be used to write captions for images, create product descriptions, generate lyrics or stories, or paraphrase sentences.

  9. Speech Recognition: This task involves converting spoken language into written text. It's used in applications like voice assistants, transcription services, and more.

  10. Natural Language Understanding (NLU): This task involves extracting the meaning and intent from natural language text or speech. For example, NLU can answer questions, extract facts, analyze opinions, or infer relationships from natural language inputs.

  11. Machine Translation: This is one of the oldest applications of NLP, which involves translating text from one language to another. Despite significant progress, it remains a challenging task due to issues like the correct translation of gendered words, differing levels of formality, or ambiguity.

These tasks form the foundation of many applications we use every day, from search engines and translation software to chatbots and voice assistants.

What are some common NLP applications?

Natural Language Processing (NLP) has a wide range of applications that are commonly used in various industries and everyday life. Here are some of the most common applications:

  1. Sentiment Analysis: This involves recognizing subtle nuances in emotions and opinions to determine how positive or negative they are. It's particularly useful for businesses to understand customer opinions about their products or services.

  2. Text Classification: This involves automatically understanding, processing, and categorizing unstructured text. It's used in various applications such as email filtering and spam detection.

  3. Chatbots & Virtual Assistants: These are designed to understand natural language and deliver an appropriate response. They are commonly used in customer service to automate responses and improve efficiency.

  4. Text Extraction: This involves automatically detecting specific information in a text, such as names, companies, places, and more. It's also known as named entity recognition.

  5. Data Summarization: NLP can be used to summarize large amounts of data, either by extracting key phrases or by creating new phrases that paraphrase the original source.

  6. Grammar Checking: NLP plays a vital role in grammar checking software and auto-correct functions. Tools like Grammarly use NLP to detect grammar, spelling, or sentence structure errors.

  7. Speech Recognition: This technology uses NLP to transform spoken language into a machine-readable format. It's an essential part of virtual assistants like Siri, Alexa, and Google Assistant.

  8. Search Engines: NLP is used in search engines to surface relevant results based on similar search behaviors or user intent.

  9. Predictive Text: NLP is used in predictive text systems to suggest the next word or phrase a user might type, based on their past typing habits.

  10. Language Translation: NLP is used in language translation applications to translate text from one language to another, helping to bridge communication gaps between different language speakers.

These applications of NLP help businesses process large amounts of unstructured data, gain insights to support decision-making, and automate time-consuming tasks.

What are some common NLP challenges?

Natural Language Processing (NLP) is a complex field that faces numerous challenges due to the inherent complexity and variability of human language. Here are some of the most common challenges:

  1. Understanding Context: Words and phrases can have different meanings depending on the context in which they are used. This makes it difficult for NLP systems to accurately interpret the intended meaning of a word or phrase.

  2. Irony and Sarcasm: These linguistic features often use words and phrases that, strictly by definition, may be positive or negative, but actually connote the opposite. This presents a significant challenge for machine learning models.

  3. Ambiguity: Human language is full of ambiguities, which can be difficult for NLP systems to handle. This includes words with multiple meanings and phrases with multiple intentions.

  4. Misspellings and Misused Words: Misspelled or misused words can create problems for text analysis. While autocorrect and grammar correction applications can handle common mistakes, they don't always understand the writer's intention.

  5. Language Differences: NLP systems often need to support multiple languages, each with its own set of vocabulary, phrasing, inflections, and cultural norms. This requires significant resources for retraining the NLP system for each language.

  6. Training Data: The quality and quantity of training data significantly impact the performance of NLP systems. If the system is fed inaccurate or skewed data, it will learn the wrong things or learn inefficiently.

  7. Innate Biases: NLP tools can carry the biases of their programmers, as well as biases within the data sets used to train them. This can lead to the reinforcement of societal biases or a better experience for certain types of users over others.

  8. Development Time: Building an NLP system requires significant time and resources, especially when developing a product from scratch.

  9. Handling Informal Language: Informal phrases, expressions, idioms, and culture-specific lingo present a number of problems for NLP, especially for models intended for broad use.

  10. Keeping a Conversation Moving: Many modern NLP applications are built on dialogue between a human and a machine. Accordingly, your NLP AI needs to be able to keep the dialogue going by asking more questions to gather more data and constantly pointing to a solution.

Despite these challenges, advancements in machine learning and AI are continually improving the capabilities of NLP systems. With ongoing research and development, many of these challenges may be overcome in the future.

What are some common NLP tools and techniques?

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between computers and human languages. It combines machine learning, computational linguistics, statistics, and deep learning models to enable computers to understand and interpret human language, both in written and spoken forms. There are several common tools and techniques used in NLP.

NLP Tools

  1. MonkeyLearn: A user-friendly, NLP-powered platform that helps you gain valuable insights from your text data.
  2. Aylien: A powerful text analysis tool that uses machine learning and NLP to extract insights from textual data.
  3. IBM Watson: Offers a range of AI-based services, each of which is stored in the IBM cloud. This versatile suite is well-suited to perform Natural Language Understanding tasks, such as identifying keywords, emotions, and categories.
  4. Google Cloud: Provides a suite of machine learning products, including pre-trained models and a platform to generate your own tailored models.
  5. Amazon Comprehend: Uses machine learning to find insights and relationships in text.
  6. NLTK (Natural Language Toolkit): An open-source Python library for NLP. It provides easy-to-use interfaces to over 50 corpora and lexical resources.
  7. Stanford Core NLP: A popular library built and maintained by the NLP community at Stanford University. It allows you to perform a variety of NLP tasks, such as part-of-speech tagging, tokenization, or named entity recognition.
  8. TextBlob: A Python library that works as an extension of NLTK, allowing you to perform the same NLP tasks in a much more intuitive and user-friendly interface.
  9. SpaCy: A fast, easy-to-use, well-documented Python library designed to handle large volumes of data. It provides a series of pre-trained NLP models.
  10. Gensim: An open-source Python library for topic modeling and document similarity analysis.

NLP Techniques

  1. Tokenization: The process of breaking down text into words, phrases, symbols, or other meaningful elements called tokens.
  2. Stemming and Lemmatization: Techniques used to reduce inflected words to their word stem or root form.
  3. Stop Words Removal: The process of eliminating common words that do not contribute to the meaning of a sentence.
  4. TF-IDF (Term Frequency-Inverse Document Frequency): A numerical statistic used to reflect how important a word is to a document in a collection or corpus.
  5. Keyword Extraction: The automated process of extracting the most relevant information from text using AI and machine learning algorithms.
  6. Word Embeddings: A type of word representation that allows words with similar meaning to have a similar representation.
  7. Sentiment Analysis: The process of determining whether a piece of writing is positive, negative or neutral.
  8. Topic Modeling: A type of statistical model used for discovering the abstract "topics" that occur in a collection of documents.
  9. Text Summarization: The process of shortening a text document with software, in order to create a summary with the major points of the original document.
  10. Named Entity Recognition (NER): A process where an algorithm takes a string of text (sentence or paragraph) as input and identifies relevant nouns (people, places, and organizations) that are mentioned in that string.

These tools and techniques are widely used in various applications of NLP, including sentiment analysis, text classification, information extraction, and machine translation. They help in transforming unstructured text data into structured data, making it easier for machines to understand and analyze.

More terms

What is an activation function?

An activation function is a mathematical function that is used to determine the output of a neural network. The function is used to map the input values (x) to the output values (y). The function is usually a sigmoid function or a rectified linear unit (ReLU).

Read more

What is stochastic optimization?

Stochastic optimization is a method of optimization that uses randomness to find an approximate solution to a problem. It is often used in problems where the search space is too large to be searched exhaustively, or when the objective function is too complex to be evaluated accurately.

Read more

It's time to build

Collaborate with your team on reliable Generative AI features.
Want expert guidance? Book a 1:1 onboarding session from your dashboard.

Start for free