computer science

Semantic role labeling

Semantic role labeling is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Semantic role labeling rather than just read about it. In short: In natural language processing, semantic role labeling (also called shallow semantic parsing or slot-filling) is the process that assigns labels to words or phrases in a sentence that indicates their semantic role in the sentence, such as that of an agent, goal, or result. It serves to find the meaning of the sentence.

Key takeaways

Semantic role labeling belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Semantic role labeling to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Semantic role labeling from memory before moving on to harder problems.

Reference excerpt

In natural language processing, semantic role labeling (also called shallow semantic parsing or slot-filling) is the process that assigns labels to words or phrases in a sentence that indicates their semantic role in the sentence, such as that of an agent, goal, or result. It serves to find the meaning of the sentence. To do this, it detects the arguments associated with the predicate or verb of a sentence and how they are classified into their specific roles. A common example is the sentence "Mary sold the book to John." The agent is "Mary," the predicate is "sold" (or rather, "to sell,") the theme is "the book," and the recipient is "John." Another example is how "the book belongs to me" would need two labels such as "possessed" and "possessor" and "the book was sold to John" would need two other labels such as theme and recipient, despite these two clauses being similar to "subject" and "object" functions.

History In 1968, the first idea for semantic role labeling was proposed by Charles J. Fillmore. His proposal led to the FrameNet project which produced the first major computational lexicon that systematically described many predicates and their corresponding roles. Daniel Gildea (Currently at University of Rochester, previously University of California, Berkeley / International Computer Science Institute) and Daniel Jurafsky (currently teaching at Stanford University, but previously working at University of Colorado and UC Berkeley) developed the first automatic semantic role labeling system based on FrameNet. The PropBank corpus added manually created semantic role annotations to the Penn Treebank corpus of Wall Street Journal texts. Many automatic semantic role labeling systems have used PropBank as a training dataset to learn how to annotate new sentences automatically.

Uses Semantic role labeling is mostly used for machines to understand the roles of words within sentences. This benefits applications similar to natural language processing programs that need to understand not just the words of languages, but how they can be used in varying sentences. A better understanding of semantic role labeling could lead to advancements in question answering, information extraction, automatic text summarization, text data mining, and speech recognition.

See also Named entity recognition Lexical semantics Semantic parsing Syntax tree

References

Jurafsky, Dan; Martin, James H. (2009). "20.9 Semantic role labeling". Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Prentice Hall series in artificial intelligence (2nd ed.). Upper Saddle River, N.J: Pearson Prentice Hall. ISBN 978-0-13-187321-6. OCLC 213375806.

External links CoNLL-2005 Shared Task: Semantic Role Labeling Illinois Semantic Role Labeler state of the art semantic role labeling system Demo Preposition SRL: Identifies semantic relations expressed by prepositions Shalmaneser is another state of the art system for assigning semantic predicates and roles.

Worked examples

Example 1 — a first encounter with Semantic role labeling

Start with the simplest possible case. Write down what Semantic role labeling claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Semantic role labeling before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Semantic role labeling ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Semantic role labeling

In research: Semantic role labeling appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Semantic role labeling in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Semantic role labeling is common in secondary-school and first-year university syllabi. It links to neighbouring topics Computational linguistics, Grammar, Tasks of natural language processing, so understanding it makes those chapters shorter.
In everyday life: Look for Semantic role labeling outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Semantic role labeling in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Semantic role labeling means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Semantic role labeling out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Semantic role labeling in simple terms?

Why does Semantic role labeling matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Semantic role labeling?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Semantic role labeling.