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\title{Predicting the Tags of Questions in StackOverflow.com}

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\begin{document}

\maketitle
\section{Overview}
As the world's most popular programmer Q\&A community, StackOverflow.com  is a showcase of the successful usage of the tag system, where each question could have one or more questions to indicate its "topics". Intuitively, We can think of the text as the distribution over tags.
 In this project, our goal is to predict the tags of a questions by mining the large amount of tagged questions.  

Furthermore, a tags predictor of high accuracy also enables us to discover user's "taste" of questions. We can estimate users interests(represented by a decimal vector) in each tag by examing questions a user asked/answered/favorited/voted. Thus, we can use this information to recommend new question or discover other user who share the similiar interests.

\section{Dataset}
StackOverflow has published their dataset under Common Creative Licence. The dataset includes over 2.2 millions questions, 4.8 million answers and 30 thousands of tags, which provides a rich content for our analysis.

\section{Investigation}
With the investigation of popular text classifcation methods, we found these approaches are promising in the area of multi-labeled supervised text classification (1) Decision Tree (2) Naïve Bayes (3) Support Vector Machines. We will adopt and improve these methods for the classification tasks.

\section{Project Roadmap}
Here's the roadmap of our project:
\begin{enumerate}
    \item Data Observation(Before mid-term): Oberserve and pre-processing the data.
    \item Investigation(Before mid-term): In-depth investigate several promising text classification methods.
    \item Feature Selection(Before mid-term): By analyzing the data, we will select the features are more likely to achieve good performance and high accuracy.
    \item Development of classification models(70\% done before mid-term)<commit_msg>Rewrite several paragraph<commit_after>\documentclass{article} % For LaTeX2e
\usepackage{nips11submit_e,times}
%\documentstyle[nips10submit_09,times,art10]{article} % For LaTeX 2.09

\title{Predicting the Tags of Questions in StackOverflow.com}

\newcommand{\fix}{\marginpar{FIX}}
\newcommand{\new}{\marginpar{NEW}}

%\nipsfinalcopy % Uncomment for camera-ready version

\begin{document}

\maketitle
\section{Overview}
As the world's most popular programmer Q\&A community, StackOverflow.com  is a showcase of the successful usage of the tag system, where each question could have one or more questions to indicate its "topics". Intuitively, We can think of the text as the distribution over tags.
 In this project, our goal is to predict the tags of a questions by mining the large amount of tagged questions.  

Furthermore, a tags predictor of high accuracy also enables us to discover user's "taste" of questions. We can estimate users interests(represented by a decimal vector) in each tag by examing questions a user asked/answered/favorited/voted. Thus, we can use this information to recommend new question or discover other user who share the similiar interests.

\section{Dataset}
StackOverflow has published their dataset under Common Creative Licence. The dataset includes over 2.2 millions questions, 4.8 million answers and 30 thousands of tags.

\section{Investigation}
<NAME>'s survey "Machine Learning in Automated Text Categorization" provides detailed information in model selection. Of all the methods,  we found these approaches are promising in the area of multi-labeled supervised text classification (1) Decision Tree (2) Naïve Bayes (3) Support Vector Machines. We will adopt and improve these methods for the classification tasks.

\section{Project Roadmap}
Here's the roadmap of our project:
\begin{enumerate}
    \item Data Observation(Before mid-term): Oberserve and pre-processing the data.
    \item Investigation(Before mid-term): In-depth investigate several promising text classification methods.
    \item Feature Selection(Before mid-term): By analyzing the data, we will select the features are more likely to achieve good performance and high accuracy.
    \item Development of classification models(70\% done before mid-term)
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{'dir': 'git-commits-cleaned', 'id': '4006273', 'n_tokens_mistral': 1167, 'n_tokens_neox': 1085, 'n_words': 563}