science

Shallow parsing

Shallow parsing is a 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 Shallow parsing rather than just read about it. In short: Shallow parsing (also chunking or light parsing) is an analysis of a sentence which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (noun groups or phrases, verb groups, etc.). While the most elementary chunking algorithms simply link constituent parts on the basis of elementary search patterns (e.g., a…

Key takeaways

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

Reference excerpt

Shallow parsing (also chunking or light parsing) is an analysis of a sentence which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (noun groups or phrases, verb groups, etc.). While the most elementary chunking algorithms simply link constituent parts on the basis of elementary search patterns (e.g., as specified by regular expressions), approaches that use machine learning techniques (classifiers, topic modeling, etc.) can take contextual information into account and thus compose chunks in such a way that they better reflect the semantic relations between the basic constituents. That is, these more advanced methods get around the problem that combinations of elementary constituents can have different higher level meanings depending on the context of the sentence. It is a technique widely used in natural language processing. It is similar to the concept of lexical analysis for computer languages. Under the name "shallow structure hypothesis", it is also used as an explanation for why second language learners often fail to parse complex sentences correctly.

References

Citations

Sources "NP Chunking (State of the art)". Association for Computational Linguistics. Retrieved 2016-01-30. Abney, Steven (1991). "Parsing By Chunks | Principle-Based Parsing" (PDF). www.vinartus.net. pp. 257–278.

External links Apache OpenNLP OpenNLP includes a chunker. GATE General Architecture for Text Engineering GATE includes a chunker. NLTK chunking Illinois Shallow Parser Shallow Parser Demo

See also Parser Semantic role labeling Named-entity recognition

Worked examples

Example 1 — a first encounter with Shallow parsing

Start with the simplest possible case. Write down what Shallow parsing claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In 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 Shallow parsing 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 Shallow parsing 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 Shallow parsing

In research: Shallow parsing appears in 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 Shallow parsing 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: Shallow parsing is common in secondary-school and first-year university syllabi. It links to neighbouring topics Natural language parsing, Natural language processing stubs, Tasks of natural language processing, so understanding it makes those chapters shorter.
In everyday life: Look for Shallow parsing 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 Shallow parsing in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Shallow parsing 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 Shallow parsing out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Shallow parsing in simple terms?

Why does Shallow parsing matter?

Because it connects several 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 Shallow parsing?

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 Shallow parsing.