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experiment/aim.md

@@ -1,12 +1,3 @@
-To understand and demonstrate the application of the Viterbi algorithm for Part-of-Speech (POS) tagging in Natural Language Processing. This experiment provides hands-on experience with the Viterbi decoding process, which is a fundamental dynamic programming algorithm used to find the most likely sequence of hidden states (POS tags) given observable sequences (words) in Hidden Markov Models.
+**To understand and practice sequence decoding for Part-of-Speech (POS) tagging using the Viterbi algorithm in Natural Language Processing.**
 
-The Viterbi algorithm is crucial in statistical NLP for solving the decoding problem: given a sequence of words and pre-computed emission and transition probabilities from a training corpus, determine the most probable sequence of POS tags that generated those words. This experiment allows learners to practice filling Viterbi tables step-by-step and understand how dynamic programming efficiently finds optimal tag sequences.
-
-For example, given the sentence "Book a park", the algorithm determines whether "Book" should be tagged as a noun or verb, considering both:
-
-- **Emission probabilities**: How likely each word is to be generated by each POS tag
-- **Transition probabilities**: How likely each POS tag is to follow another in sequence
-
-Through interactive simulation, learners will master the mathematical foundations of the Viterbi algorithm and its practical application in modern POS tagging systems.
-
-<img src="images/viterbi-4.gif" alt="Viterbi Decoding Animation" style="display:block;margin:auto;max-width:400px;">
+This experiment aims to help students develop proficiency in applying the Viterbi algorithm to find the most probable sequence of POS tags for a given sentence, using emission and transition probabilities. Through interactive exercises, learners will gain hands-on experience with dynamic programming and sequence labeling in NLP.

experiment/assignment.md

@@ -14,21 +14,21 @@
 
 **Emission Matrix P(word|tag):**
 
-```
+<pre>
          The    dog    runs
 Noun     0.1    0.6    0.1
 Verb     0.0    0.1    0.8
 Det      0.9    0.0    0.0
-```
+</pre>
 
 **Transition Matrix P(tag_j|tag_i):**
 
-```
+<pre>
          Noun   Verb   Det
 Noun     0.3    0.4    0.1
 Verb     0.4    0.1    0.2
 Det      0.7    0.2    0.1
-```
+</pre>
 
 Assume equal initial probabilities π[tag] = 1/3 for all tags.