Update README with more information

Author: AaronCCWong

.gitignore

@@ -1,5 +1,4 @@
 bin/
-WSJ_24.pos
 .vscode
 .settings
 .classpath

README.md

@@ -1,6 +1,10 @@
-# Viterbi POS Tagger
+# HMM POS Tagger with Viterbi Decoding
 
-Tested using Java 11.
+## To Run
+
+This was primarily tested on Java 11.
+
+### Compile
 
 To run from the command line, make sure you are in the root directory `viterbi`. This is the directory that contains `src` and `WSJ_POS_CORPUS_FOR_STUDENTS`.
 Run
@@ -9,11 +13,22 @@ Run
 javac src/viterbi/*.java
 ```
 
+### Run
+
 Then to train and evaluate
 
 ```bash
 java -cp src viterbi.WSJPOSTagger WSJ_POS_CORPUS_FOR_STUDENTS/WSJ_02-21.pos TEST_FILE MAX_SUFFIX_LENGTH MAX_WORD_FREQUENCY
 ```
 
 where `TEST_FILE` is the file with sentences that you want to tag, `MAX_SUFFIX_LENGTH` is the maximum suffix length to use for the suffix tree and
-`MAX_WORD_FREQUENCY` is the maximum word frequency as found in the training set of the words to use for the suffix tree.
+`MAX_WORD_FREQUENCY` is the maximum word frequency as found in the training set of the words to use for the suffix tree.
+
+## Implementation Details
+
+This is a Hidden Markov Model part of speech tagger that uses the Viterbi algorithm for decoding.
+The model is trained on the Wall Street Journal POS corpus and attempts to handle unknown words by performing suffix analysis using suffix trees as described by [(Brants, 2000)](#brants).
+
+## References
+
+<a id="brants"></a> Brants, T. (2000). TnT: A statistical part-of-speech tagger. In *ANLP 2000*, Seattle, WA, pp. 224–231.