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Top 10 Syntactic Analysis MCQs in NLP (With Detailed Explanations)
Syntactic analysis is a core component of Natural Language Processing (NLP), enabling machines to understand grammatical structure and word relationships. This post presents 10 carefully selected multiple-choice questions (MCQs) on syntactic parsing, dependency structures, neural parsing, and modern NLP concepts. Each question includes a clear explanation to help students prepare for exams, interviews, and competitive tests.
Many sentences in natural language are ambiguous, meaning they can have multiple valid parse trees. For instance, structural ambiguity produces multiple parse trees. Consider an example sentence "I saw a man with a telescope" which can be interpreted as follows;
- I used a telescope to see the man.
- The man I saw had a telescope.
So the parser must choose the most likely structure, not just any valid one. Probabilistic parsers assign probabilities and choose the most likely structure.
Why probabilistic parsers?
They assign probabilities to grammar rules or parse trees. Evaluates all possible parses. Selects the most probable parse.
Example probabilisitc parsers:
- PCFG (Probabilistic Context-Free Grammar)
- Neural dependency parsers with scoring
Because ambiguity results in multiple possible parses, we need a ranking mechanism and probabilities provide that.
Dependency trees directly model head–dependent relations, making them simpler, efficient, and useful for downstream tasks like translation and information extraction. Dependency parsing is preferred in modern NLP because it gives simple, efficient structures with direct word-to-word relationships.
What is Constituency Parsing?
Constituency parsing divides a sentence into phrases (constituents) based on grammar rules.
Example:
"The boy ate an apple".
Structure:
[ Sentence
[Noun Phrase: The boy]
[Verb Phrase: ate [Noun Phrase: an apple]]
]
The main focus of constituency parsing is on phrase structure (NP, VP, PP, etc.)
What is Dependency Parsing?
Dependency parsing shows direct relationships between words.
Example: ate → root; boy → subject of ate (nsubj); apple → object of ate (obj); The → modifier of boy;
The main focus of dependency parsing is to capture: Who depends on whom (word-to-word relations)
Main advantage of dependency parsing over constituency parsing for modern NLP systems
Modern NLP tasks (machine translation, information extraction, question answering, etc.) mainly need: Direct word relationships, Simpler structure, Computational efficiency.
Why dependency parsing is better?
Fewer nodes (only words, no extra phrase nodes), Simpler trees, Direct relations like subject, object, modifier, and Faster and easier for machine learning models
Why other options are INCORRECT?
Option A: Captures phrase boundaries more precisely. INCORRECT. That is the strength of constituency parsing, not dependency.
Option C: Requires no training data. INCORRECT. Modern parsers require training.
Option D: Works only for English. INCORRECT. Dependency parsing works for many languages.
Non-projective structures occur when dependency arcs cross. Crossing arcs indicate that the sentence structure cannot be represented using projective constraints. Some languages require parsers that can handle such structures. More can be found below.
What is dependency parser?
A dependency parser analyzes the grammatical structure of a sentence by identifying head–dependent relationships between words. Each word (except the root) depends on another word (its head). The structure forms a dependency tree. Example: “Ram wrote a letter”. Here, "wrote" is root, "Ram" is subject of wrote, and "letter" is object of wrote.
Dependency parsing focuses on word-to-word relations, which is very useful for modern NLP tasks.
What are Projective and Non-Projective Dependency parsing?
- Projective dependency: A dependency tree is projective if when dependencies are drawn as arcs above the sentence and no arcs cross each other. All relations can be drawn without crossing. Projective structures are common in fixed word-order languages like English.
- Non-projective dependency: A dependency tree is non-projective if some dependency arcs cross when drawn over the sentence. This often happens due to free word order, long-distance dependency and scrambling (common in languages like Hindi, German, Czech, Tamil, etc.). Some dependencies cross when drawn. Non-projective parsing is needed to correctly represent such structures.
Why other options are INCORRECT?
Sentence length (Option A) does not cause non-projectivity.
Unknown words (Option C) relate to vocabulary issues, not structure.
Grammar ambiguity (Option D) affects interpretation but does not necessarily create crossing dependencies.
Transition-based parsers make local decisions. Early mistakes cannot be corrected later (The parser cannot go back and fix the mistake), leading to error propagation.
What are Greedy Transition-Based Parsers?
Transition-based dependency parsers build a dependency tree step-by-step using a sequence of actions (such as, SHIFT, LEFT-ARC, RIGHT-ARC, REDUCE). A greedy parser chooses the best action at each step based only on current information. It does not reconsider previous decisions. They are very fast and memory-efficient.
Why other options are INCORRECT?
High memory usage (Option A) Greedy parsers use low memory.
Cannot handle short sentences (Option B) Works for any sentence length.
Cannot produce dependency trees (Option D) Produces trees efficiently.
Graph-based parsers evaluate possible trees globally and select the highest-scoring structure, reducing greedy errors.
Transition-Based vs Graph-Based dependency parsing
- Transition-Based Parsing: Builds the dependency tree step-by-step. Uses a stack, buffer, and actions (SHIFT, LEFT-ARC, RIGHT-ARC). Decisions are local and incremental.
- Graph-Based Parsing: Treats parsing as a global optimization problem. Considers all possible head–dependent arcs. Assigns a score to the entire tree. Selects the highest-scoring valid tree (often using algorithms like MST – Maximum Spanning Tree)
Why other options are INCORRECT?
Builds the tree incrementally using a stack (Option A) This describes transition-based parsing, not graph-based.
Uses no machine learning (Option C) Modern graph-based parsers heavily use machine learning (neural networks).
Works only for projective trees (Option D) Many graph-based methods can handle non-projective trees (e.g., MST parser).
Contextual embeddings capture agreement, phrase boundaries, and long-distance dependencies, improving parsing accuracy. BERT helps parsers by understanding each word based on its context, which improves detection of grammatical relationships.
Why embeddings are important in modern neural parsers?
Neural parsers (dependency or constituency) work with numbers, not words. So each word must be converted into a vector representation — this is called an embedding.
Embeddings are essential in modern neural parsers because they:
- Convert words into numerical input
- Capture semantic and syntactic information
- Provide contextual understanding (with BERT)
- Significantly improve parsing performance.
Dependency parsing determines which word acts as the head and which is the dependent for each relationship.
What is the head selection problem?
In dependency parsing, for each word, the parser must decide "Which word is its head?". This decision is called head selection. For example, given a sentence "She saw a dog", for the word "dog", the parser must decide does "dog" depend on "saw"? Or on "a"?. So the head selection task is about choosing the governing word for each word.
Head selection = deciding which word is the head (governor) for each word in the sentence.
They struggle because the number of possible parses and computational cost grow rapidly with sentence length, making long-distance dependencies hard to handle.
Traditional parsers struggle with long sentences because:
- Too many possible structures
- High computational cost
- Difficulty handling long-distance relationships
- Error propagation
The Universal Dependencies (UD) framework is designed to create a consistent and language-independent way to represent grammatical structure (syntactic annotaion) across many languages.
Different languages have different grammar. UD provides a common set of rules and labels so that dependency structures look similar across languages and the same annotation scheme is used worldwide.
Even without explicit parsing, LLMs learn syntax implicitly, helping capture agreement, clause structure, and long-distance dependencies.
More explanation:
Large Language Models (LLMs) need to understand how words in a sentence are related to each other. In many sentences, important grammatical relationships occur between words that are far apart.
Example:
The book that the student bought yesterday is interesting.
The verb is agrees with book, not with student or yesterday. This is called a long-range dependency.
Syntactic signals (such as dependency relations or structural patterns) help the model:
- Identify subject–verb and modifier relationships
- Understand sentence structure
- Maintain grammatical consistency
- Handle complex and long sentences
Without syntactic information, the model may rely only on nearby words and miss these long-distance relationships.