What natural language processing is

Three things you probably did this week. You said "hey Siri, set an alarm for six" and a phone acted on it. A visitor pointed Google Translate's camera at an Arabic menu and read it in English. And Gmail moved a phishing message into spam before you ever saw it. All three are natural language processing, and they are three different jobs: a human talking to a machine, two humans with a machine in the middle, and no conversation at all, just text being handled usefully.

That is exactly how the field defines itself. Slide 4 states it in one sentence: NLP is a branch of artificial intelligence that is concerned with interactions between computers and human language. The purpose clause that follows is the part to memorise, because it lists the three goals in a fixed order: enabling human-machine communication, improving human-human communication, or simply doing useful processing of text or speech. The wording is lifted almost verbatim from the opening of Jurafsky and Martin's textbook, which describes the goal of the field as getting computers to perform useful tasks involving human language, "tasks like enabling human-machine communication, improving human-human communication, or simply doing useful processing of text or speech" (Jurafsky and Martin, 2nd edition, chapter 1).

Notice what the definition does not say. It does not name a technique. It does not say statistics, or neural networks, or grammars. NLP is defined by its problem, human language, and by what it wants to achieve with it. The techniques have moved from hand-written rules to statistical learning to deep neural networks (part 03 walks through that sequence), and the definition has not moved with them. That is why it is worth learning by heart: it will still be true when the current models are obsolete.

The logos, sorted by goal

Slide 2 shows seven companies and says nothing else, so the exercise is to say it for them. Each logo stands for at least one product whose core step is a language step, and each of those products serves one of the three goals. Google Search is the clearest case: in 2019 Google wrote that "Search is about understanding language" and introduced BERT to help it understand one in 10 searches in the U.S. in English better (Google, 2019). Amazon's developer documentation describes the Alexa pipeline as streaming your speech to the cloud, where "the Alexa service handles the speech recognition and natural language processing" (Amazon). Meta's No Language Left Behind project calls machine translation "a key focus of artificial intelligence research today" and covers 200 languages (NLLB Team, 2022). Grammarly says its team of computational linguists and deep learning engineers builds algorithms that learn "the rules and hidden patterns of good writing by analyzing millions of sentences" (Grammarly), which is spelling and grammar correction at industrial scale.

One field, many names

Slide 4 ends by admitting that the field "has many names" and lists three. They are not rival fields. Each stresses a different facet of the same work, and the facet tells you who is speaking. When someone says computational linguistics they are usually facing the linguistics side: language as the object of scientific study, with computation as the method. Its professional society is the ACL, which describes itself as serving people working on computational problems involving human language, "a field often referred to as either computational linguistics or natural language processing (NLP)" (ACL). When someone says speech and language processing they are quoting the title of the Jurafsky and Martin book and reminding you that speech is in scope. When someone says human language technology they are on the engineering side, thinking about systems that ship.

The list keeps growing. The current draft of the third edition says the field "has many names corresponding to its many facets, including natural language processing (NLP), computational linguistics, spoken language processing, speech recognition, or most recently simply language modeling" (Jurafsky and Martin, 3rd edition draft, chapter 1). Note the word "recently": language modeling is the newest label, not a replacement for the field.

Take the spam filter again and ask what you would need to build one. You need to know what counts as a word, that free and FREE!!! are the same word dressed differently, and that "not a scam" flips the meaning of "scam". That is linguistics. You need a classifier, a hash table for the vocabulary, and an algorithm that runs in reasonable time on millions of messages. That is computer science. And you need a way to learn the weights from mail people have reported, and to represent the result as "spam with probability 0.97". That is artificial intelligence.

Slide 5 draws this as a Venn diagram: NLP sits at the intersection of linguistics, computer science (abbreviated Comp. Sci. on the slide) and AI, and the bullet list trails off with an ellipsis. The ellipsis is not decoration. Read against slide 24, which names the fields NLP draws its models, algorithms and theories from as computer science, mathematics and linguistics, the ellipsis stands most plausibly for mathematics (the part of it NLP leans on most is probability and statistics). Jurafsky and Martin go further, listing the foundations of the field as "computer science, linguistics, mathematics, electrical engineering, and psychology" (Jurafsky and Martin, 2nd edition, chapter 1). Electrical engineering enters through the signal processing that speech needs, and psychology through the study of how people actually produce and understand language.

What each parent contributes

The three circles are not interchangeable, and an exam question will often ask you to say what each one brings. Linguistics supplies the object of study and the vocabulary for describing it: the levels of language from sounds through words and sentences to meaning and discourse, the theories of grammar that say which sentences are well formed, and the very idea of ambiguity, which part 04 returns to. Computer science supplies the machinery. Jurafsky and Martin single out "state space search algorithms, such as dynamic programming, and machine learning algorithms, such as classifiers" as the most important (Jurafsky and Martin, 2nd edition, section 1.3). AI supplies learning and knowledge: how to fit a model to data, how to store facts a program can reason over, and the probabilistic stance that, given several possible readings, you should choose the most probable one.

Jurafsky and Martin make the point with the Unix program wc. When it counts bytes and lines it is ordinary data processing. When it counts words it is doing something linguistic, because it needs to know what a word is. The line between data processing and language processing is exactly the linguistics circle: the moment your program relies on knowledge of language, it has crossed into NLP.

Photograph a street sign in Riyadh with your phone. Vision finds the sign in the pixels and reads its letters. NLP translates the words on it. A delivery robot could be told "turn left at that sign" and act on it. And a knowledge base knows that the name on the sign is a real district with a location and a history. One photo has just touched four subfields of artificial intelligence, and most of those steps are now learned from data by a fifth.

Slide 6 lists those five subfields as stacked boxes: natural language processing, knowledge representation and deduction, machine learning, computer vision and robotics. Only one box has a sub-bullet: deep learning is indented under machine learning. The same map appears in the standard AI textbook. Russell and Norvig give knowledge representation its own chapter inside a part called "Knowledge, reasoning, and planning", give machine learning its own part with a deep learning chapter inside it, and group natural language processing, computer vision and robotics under "Communicating, perceiving, and acting" (Russell and Norvig, 4th edition, table of contents). The slide is the textbook's contents page compressed to five lines.

• Natural language processing
• Knowledge representation and deduction (standard phrase: reasoning)
• Machine learning, with deep learning as its sub-area
• Computer vision
• Robotics

Sibling and tool at once

The key point of the slide is why deep learning is indented rather than listed beside the others. Machine learning is a subfield in its own right, but it is also the main method NLP, computer vision and robotics now use, and an increasingly common one in knowledge representation as well. Goodfellow, Bengio and Courville draw this as nested sets: deep learning is a kind of representation learning, which is a kind of machine learning, "which is used for many but not all approaches to AI" (Goodfellow et al., 2016, figure 1.4). The nesting is why the slide indents deep learning, and the "used for" clause is why the AI textbook has both a chapter called Natural Language Processing and one called Deep Learning for Natural Language Processing. It is also why the rest of ICS 582 is mostly machine learning: the problem is language, the method is learning.

The pairings between NLP and its siblings are concrete systems you can name in an exam. With computer vision, image captioning: Vinyals and colleagues describe automatically describing an image as a problem that "connects computer vision and natural language processing" (Vinyals et al., 2014). Optical character recognition is the other direction, a vision task that extracts "printed or handwritten text from images such as posters, street signs, and product labels" and hands it to NLP (Microsoft Learn). With robotics, instruction following: the SayCan system completes "long-horizon, abstract, natural language instructions on a mobile manipulator", with the robot acting as the language model's "hands and eyes" (Ahn et al., 2022). With knowledge representation, question answering over a knowledge graph: Google's Knowledge Graph understands "real-world entities and their relationships to one another: things, not strings", and uses that to tell the Taj Mahal monument from the musician (Google, 2012).