Summary of "POS Tagging | Part of Speech Tagging in NLP | Hidden Markov Models in NLP | Viterbi Algorithm in NLP"

POS tagging tutorial — HMMs and Viterbi (video lecture)

Overview

This video explains part-of-speech (POS) tagging and demonstrates both practical tooling (spaCy) and the statistical core behind taggers (Hidden Markov Models and the Viterbi algorithm). It includes a hands-on demo, a worked toy example, and practical advice for building and inspecting taggers.

POS tagging: assigning a POS tag to every word in a sentence (coarse- and fine-grained tags).

What the video covers (high-level)

Definition and importance
- POS tagging assigns POS tags to each word in a sentence (coarse- and fine-grained).
- Common preprocessing step for NER, information retrieval, question-answering, word-sense disambiguation, chatbots, and other NLP pipelines.
Tools / demo
- Hands-on spaCy demo: installing spaCy, loading en_core_web_sm, creating a Doc, accessing token.text, token.pos_, token.tag_, using spacy.explain(), looping through tokens, and visualizing tags with displaCy.
- Shows coarse vs fine-grained tags and context-dependent tag examples (e.g., words like “left” and “read” that change tag by context).
Core algorithms
- Statistical POS tagging via Hidden Markov Models (HMMs): train on labeled data, compute emission (observation) probabilities and transition probabilities (use start/end tokens), and use these probabilities to score tag sequences.
Brute-force vs optimized decoding
- Brute-force enumeration of all tag sequences grows as |T|^n and is intractable for realistic sentences.
- The Viterbi algorithm uses dynamic programming: at each timestep keep the most probable path to each state, then backtrack to recover the best tag sequence.
Worked example
- Manual toy dataset: compute counts → normalize to emission/transition probabilities → compute sequence probabilities for candidate tag assignments.
- Shows how zero or small probabilities prune/penalize paths and how Viterbi reduces computation while selecting the most probable tag path.
Practical tips
- In practice, spaCy performs tagging and visualization.
- The HMM/Viterbi walkthrough helps understand what libraries do internally.
- When building taggers, experiment and inspect probabilities; smoothing is important to handle zeros.

Key technical concepts explained

Emission probabilities
- P(word | tag) computed from counts and normalized across words for each tag.
Transition probabilities
- P(tag_t | tag_{t-1}) computed from tag sequence counts; include start/end markers to model sentence boundaries.
Sequence scoring
- The probability of a tag sequence = product of emission and transition probabilities for the sequence; choose the sequence with maximum probability.
Viterbi algorithm
- Dynamic programming approach to avoid exponential brute-force enumeration.
- Maintain the best predecessor and probability per tag per position; backtrack from the end to recover the tags.
Visualization
- displaCy options (distance, colors, options dictionary) to render POS annotations clearly.

Code / commands demonstrated (spaCy)

Install and load:

pip install spacy
python -m spacy download en_core_web_sm

Python demo:

import spacy
nlp = spacy.load("en_core_web_sm")
doc = nlp("I will Google about Facebook")

# token access
for token in doc:
    print(token.text, token.pos_, token.tag_, spacy.explain(token.tag_))

displaCy rendering:

from spacy import displacy
displacy.render(doc, style="dep")      # dependency visualization
displacy.render(doc, style="ent")      # entity visualization
# displaCy supports options such as colors and spacing via an options dict

Notes:

Access token attributes with doc[i].text, doc[i].pos_, doc[i].tag_, and spacy.explain(doc[i].tag_).
Loop through tokens to print coarse/fine tags and explanations.

Worked example (toy dataset)

Start with a few labeled sentences and count tag-to-tag transitions and tag-to-word emissions.
Convert counts into emission and transition probabilities (normalization).
Compute probabilities for candidate tag sequences. Low or zero probabilities eliminate or penalize candidate sequences.
Apply Viterbi to efficiently find the highest-probability tag sequence by keeping the best path to each state at each timestep and backtracking at the end.

Practical tips

Use spaCy for production and visualization; it handles tagging efficiently.
Study HMM/Viterbi to understand internal behaviors of taggers and to debug or customize models.
Inspect raw counts and probabilities; apply smoothing where appropriate to avoid zero probabilities.
Run experiments to observe how context affects tagging (e.g., words like “left”, “read”).

Recommended resources

The presenter suggests watching a dedicated video on Hidden Markov Models before the detailed HMM/Viterbi explanation. The referenced channel name in captions may be garbled, but look for a clear HMM tutorial (examples: university lecture videos or concise HMM overviews from reliable tutorial channels).

Notes on caption inaccuracies

Several auto-generated subtitles contained garbled words/names (e.g., “Twitter”, “Iodine”, “Marg”) that are likely mis-transcriptions of technical terms such as “trigram”, “smoothing”, etc.
Despite subtitle errors, the technical core (spaCy demo, emission/transition probabilities, Viterbi optimization) is consistent and should be the focus.

Main speaker / sources

Presenter: “Mike” (name appears in captions; likely the channel instructor).
Libraries/tools referenced: spaCy (en_core_web_sm), displaCy visualizer.
External tutorial recommended: an HMM/HMM-to-Viterbi explanatory video (caption referenced something like “Neso Academy” / “No Academy” — seek a clear HMM tutorial prior to deeper explanations).