# Q7a · POS, chunks and boundary-aware HMM

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/papers/legacy-f24/q7a

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q7a/

Previous examination · complete worked answers

**Question. Define function/content words, chunking/chinking and POS applications. Construct transition and emission tables including start/end for: Book/Verb a/Det flight/Noun; Read/Verb the/Det book/Noun; The/Det book/Noun is/Modal-Verb green/Noun.** [10 marks]

### Answer

**Part-of-speech (POS) tagging** assigns a grammatical category to every word token in context, such as noun, verb, adjective or determiner. It is a sequence-labeling task: the input is a sequence of words, and the output is a tag for each word. A tag describes the word's role in this sentence, not its meaning in every possible sentence.

| Tag | Role | Example in context |
| --- | --- | --- |
| NN / NNS | Singular / plural common noun | a book / two books |
| NNP | Proper noun | Mary, Mumbai |
| VB / VBD / VBZ | Base / past / third-person singular verb | read / read / reads |
| JJ | Adjective | the red book |
| RB | Adverb | runs quickly |
| DT | Determiner | the, a, this |
| IN | Preposition or subordinating conjunction | on the table |
| PRP | Personal pronoun | she, they |
| MD | Modal auxiliary | can, will, should |

**Open classes** readily accept new content words: nouns, verbs, adjectives and adverbs. Examples include chatbot and livestream. **Closed classes** contain comparatively stable grammatical/function words: determiners, pronouns, prepositions and conjunctions. These are tendencies, not a claim that language never changes.

Input: Book that flight

→

Context: an imperative request

→

Output: Book/VB that/DT flight/NN

Compare “hand me that **book**”: book is a noun here. Similarly, “I **can** swim” has can/MD, while “the **can** is empty” has can/NN. The spelling stays the same; neighboring words and sentence structure change the correct tag. This is **POS ambiguity**.

| Sentence | Tagged interpretation | Reason |
| --- | --- | --- |
| They watch birds. | They/PRP watch/VBP birds/NNS | watch is the action. |
| The watch stopped. | The/DT watch/NN stopped/VBD | watch is the object that stopped. |
| She has a light bag. | She/PRP has/VBZ a/DT light/JJ bag/NN | light describes bag. |
| Light the lamp. | Light/VB the/DT lamp/NN | Light is an imperative verb. |

POS tags support parsing, named entity recognition, question answering, sentiment analysis and word-sense disambiguation. They do not by themselves identify every entity or fully resolve meaning.

**Yaad rakho:** Word ka naam same ho sakta hai; sentence mein uska kaam tag decide karta hai.

**Chunking**, also called shallow parsing, groups adjacent tagged words into meaningful non-overlapping units, commonly noun phrases. It usually identifies local phrase boundaries without producing the complete hierarchical structure of a sentence. **Chinking** removes specified tokens from an existing candidate chunk, splitting or trimming it.

The/DT small/JJ cat/NN sat/VBD on/IN the/DT mat/NN

→

NP: [The small cat]

→

Outside: sat on

→

NP: [the mat]

A POS tagger labels individual words. A chunker groups words into phrases. A full parser shows how phrases relate recursively to form the entire sentence. These are related tasks with different outputs.

| Operation | Input | Output |
| --- | --- | --- |
| POS tagging | The small cat | The/DT small/JJ cat/NN |
| NP chunking | Tagged tokens | [NP The small cat] |
| Chinking | A broad candidate chunk | Remove verbs/prepositions, keep noun-phrase segments |
| Full parsing | Sentence + grammar | A nested tree with sentence, noun phrase and verb phrase nodes |

**Rule-based chunking** uses tag patterns. Statistical or learned chunkers infer boundaries from labeled examples. A useful NP rule is an optional determiner, followed by zero or more adjectives, followed by a noun. In NLTK-style notation: `NP: {<DT>?<JJ>*<NN>}`. Here ? means zero or one occurrence; \* means zero or more; + means one or more when used. The braces mean “form a chunk around the matched sequence”.

**Question. Apply the NP rule optional DT, zero or more JJ, then one NN to “The/DT quick/JJ brown/JJ fox/NN jumps/VBZ over/IN the/DT lazy/JJ dog/NN”.**

#### Given

Tagged input is supplied. Rule: DT? JJ\* NN.

#### To find

All noun-phrase chunks.

#### Formula and why it applies

`NP → DT? JJ* NN`

The rule groups a noun with its preceding determiner and adjectives; a verb or preposition does not match this NP pattern.

#### Solution

##### Step 1. Start at The

The matches optional DT; quick and brown both match JJ\*; fox matches NN.

##### Step 2. Form the first NP

**[NP The quick brown fox]**

##### Step 3. Skip nonmatching tokens

jumps/VBZ and over/IN do not satisfy the NP pattern.

##### Step 4. Match the second NP

the is DT, lazy is JJ, dog is NN: **[NP the lazy dog]**.

**Final answer.** [NP The quick brown fox] jumps over [NP the lazy dog].

**Question. Using NP → DT? JJ\* NN, identify the chunks in “bright/JJ stars/NN shine/VBP and/CC a/DT moon/NN glows/VBZ”. Use the supplied tags exactly.**

#### Given

Input tags label stars as NN for this exercise. Rule requires optional DT, zero or more JJ, then NN.

#### To find

The matched NP spans.

#### Formula and why it applies

`NP → DT? JJ* NN`

The optional determiner may be absent and the adjective repetition may be empty.

#### Solution

##### Step 1. Match bright stars

No determiner is present, which DT? permits. bright matches JJ; stars matches NN.

##### Step 2. Inspect shine and and

VBP and CC fall outside the NP rule.

##### Step 3. Match a moon

a matches DT; no adjective is present; moon matches NN.

**Final answer.** Chunks are [NP bright stars] and [NP a moon]. With ordinary Penn tagging, stars is usually NNS; broaden the noun pattern to include NNS when that tag is used.

**Question. Begin with one candidate chunk containing “The/DT cat/NN sat/VBD on/IN the/DT mat/NN”. Chink out verbs and prepositions. Show the remaining chunks.**

#### Given

Initial candidate: [The cat sat on the mat]. Remove tags VBD and IN.

#### To find

The chunk segments after exclusion.

#### Formula and why it applies

Chinking removes matched tokens from the existing chunk.

Removing the middle verb/preposition region divides the original candidate into two remaining contiguous spans.

#### Solution

##### Step 1. Locate the tokens to remove

sat is VBD and on is IN.

##### Step 2. Remove those tokens from chunk membership

The remaining token groups are “The cat” and “the mat”. sat and on still exist in the sentence; they are simply outside these chunks.

##### Step 3. Write the final chunked sentence

**[The cat]** sat on **[the mat]**.

**Final answer.** Two chunks remain: The cat; the mat.

**Yaad rakho:** Chunking = andar group karo; chinking = unwanted tokens ko group se bahar karo.

### Exact HMM table

### Given

| Sentence | Supplied tags |
| --- | --- |
| Book a flight | Verb Det Noun |
| Read the book | Verb Det Noun |
| The book is green | Det Noun Modal-Verb Noun |

### To find

Transition and emission probability tables including START and END.

### Formula: which one and why



$$
a_{ij}=\frac{c(t_i,t_j)}{c(t_i)},\qquad b_i(w)=\frac{c(t_i,w)}{c(t_i)}
$$



For START divide by sentence count. END has no outgoing or emission distribution. Keep the supplied tags even when their linguistic choice is unusual, and retain case-sensitive Book/book and The/the as printed.

### Step 1. Add boundary tags to every sentence

START Verb Det Noun END  
START Verb Det Noun END  
START Det Noun Modal-Verb Noun END

### Step 2. Count transitions

| From → to | Count |
| --- | --- |
| START → Verb | 2 |
| Verb → Det | 2 |
| Det → Noun | 3 |
| Noun → END | 3 |
| START → Det | 1 |
| Noun → Modal-Verb | 1 |
| Modal-Verb → Noun | 1 |

### Step 3. Divide by each source tag count

| From | Denominator | Verb | Det | Noun | Modal-Verb | END |
| --- | --- | --- | --- | --- | --- | --- |
| START | 3 | 2/3 | 1/3 | 0 | 0 | 0 |
| Verb | 2 | 0 | 1 | 0 | 0 | 0 |
| Det | 3 | 0 | 0 | 1 | 0 | 0 |
| Noun | 4 | 0 | 0 | 0 | 1/4 | 3/4 |
| Modal-Verb | 1 | 0 | 0 | 1 | 0 | 0 |

### Step 4. Count and normalize emissions

| Tag | Count | Book | a | flight | Read | the | book | The | is | green |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Verb | 2 | 1/2 | 0 | 0 | 1/2 | 0 | 0 | 0 | 0 | 0 |
| Det | 3 | 0 | 1/3 | 0 | 0 | 1/3 | 0 | 1/3 | 0 | 0 |
| Noun | 4 | 0 | 0 | 1/4 | 0 | 0 | 1/2 | 0 | 0 | 1/4 |
| Modal-Verb | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |

### Final answer

Every nonterminal transition row sums to1 when END is included; every emission row sums to1. Boundary symbols do not emit lexical words.

POS applications include syntax parsing, disambiguation, lemmatization, chunking and extraction. For this numerical retain printed green=Noun and is=Modal-Verb even though ordinary linguistic tagging would differ; the task is estimation from supplied labels.

**Source / convention note.** Printed tags are used as training data; START and END are explicitly required and included.