Merge pull request #1 from Anmol-Singh-Jaggi/master

Humble request

Author: goelakash

README.md

@@ -1,40 +1,41 @@
-HelpBot (A work in progress)
+# HelpBot
 
-A simple QA system for the information-retrieval that attempts to solve user queries using a corpus of data.
+(A work in progress)
 
-How it works?
+A simple QA system for information-retrieval that attempts to solve user queries using a corpus of data.
 
-1. Extract HelpBot.zip and cd into the directory and run the following commands:
-  a. export FLASK_APP=server.py
-  b. flask run
-2. Goto browser and type "localhost:5000" (default port is 5000)
+**How does it work?**
+
+1. Extract `HelpBot.zip` and cd into the directory and run the following commands:  
+  a. `export FLASK_APP=server.py`  
+  b. `flask run`
+2. Open browser and type "localhost:5000" (default port is 5000)
 3. Enter your search query into the text-box and click submit.
-4. You'll see results in the following format:
-  a. The entered query in <BLUE> color
+4. You'll see results in the following format:  
+  a. The entered query in <BLUE> color  
   b. A set of matching questions in <RED> and answers in <GREEN>
-5. User can see which question is nearest to his search query and follow those steps.
+5. User can see which question is closest to his search query and follow those steps.
 
 
-Algorithm:
+**Algorithm:**
 
 The algorithm is a type of unsupervised learning.
-The logic for matching the user query is based on the cosine-similarity of the query with existing questions/queries in the dataset. The process consists of two parts:
+The logic for matching the user query is based on the cosine-similarity of the query with existing questions/queries in the dataset.
+The process consists of two parts:
 
-1. Precomputation
-  a. The text of the docs in SampleDocuments.zip was separated into pairs of questions and their answers.
-  b. The text of the questions was cleaned and stemmed and saved in a separate file "stemmed_questions.bin"
+1. Precomputation  
+  a. The text of the docs in `SampleDocuments.zip` was separated into pairs of questions and their answers.  
+  b. The text of the questions was cleaned and stemmed and saved in a separate file "stemmed_questions.bin".
 
-2. Realtime Matching
-  a. The input query is also stemmed 
-  b. The stemmed questions are loaded from disk and used to created a TF-IDF matrix
-  c. The stemmed input query is converted to a TF-IDF vector using the matrix created above (the tfidf matrix' vocabulary is set during the step 2 above)
-  d. The product of the input query vector with each question vector (from the TF-IDF matrix) is calculated, and the results  are sorted by descending proudct score.
-  e. Top 5 scoring questions are returned, as they seem most relevant to user-query acoording to TF-IDF measure.
+2. Realtime Matching  
+  a. The input query is also stemmed.  
+  b. The stemmed questions are loaded from disk and used to created a TF-IDF matrix.  
+  c. The stemmed input query is converted to a TF-IDF vector using the matrix created above (the tfidf matrix' vocabulary is set during the previous step).  
+  d. The product of the input query vector with each question vector (from the TF-IDF matrix) is calculated, and the results are sorted by descending product score.  
+  e. Top 5 scoring questions are returned, as they seem most relevant to user-query according to TF-IDF measure.
 
 
-Improvements:
+**Improvements:**
 
 1. Currently on questions are being considered. We can also gather context from answers to get better results.
-2. We are currently relying on exact words. This will fail in case the use-query has synonyms of words in the knowledge-database. We can correct this by converting the questions and answers data to word-embeddings using word2vec algo. Using this, we could find the most relevant questions in the database with the smallest Euclidean distance to the word2vec vector of the user-query, and should theoretically fix this edge-case.
- 
- 
+2. We are currently relying on exact words. This will fail in case the use-query has synonyms of words in the knowledge-database. We can correct this by converting the questions and answers data to word-embeddings using word2vec algo. Using this, we could find the most relevant questions in the database with the smallest Euclidean distance to the **word2vec** vector of the user-query, and should theoretically fix this edge-case.