-
Notifications
You must be signed in to change notification settings - Fork 1
Expand file tree
/
Copy pathindex.html
More file actions
191 lines (190 loc) · 24.9 KB
/
index.html
File metadata and controls
191 lines (190 loc) · 24.9 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang xml:lang>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
<title>Yuanbin Wu </title>
<style>
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
.display.math{display: block; text-align: center; margin: 0.5rem auto;}
</style>
<style type="text/css">html { font-size: 100%; overflow-y: scroll; -webkit-text-size-adjust: 100%; -ms-text-size-adjust: 100%; }
body{
color:#444;
font-family:Georgia, Palatino, 'Palatino Linotype', Times, 'Times New Roman', serif;
font-size:12px;
line-height:1.5em;
padding:1em;
margin:auto;
max-width:42em;
background:#fefefe;
}
a{ color: #0645ad; text-decoration:none;}
a:visited{ color: #0b0080; }
a:hover{ color: #06e; }
a:active{ color:#faa700; }
a:focus{ outline: thin dotted; }
a:hover, a:active{ outline: 0; }
::-moz-selection{background:rgba(255,255,0,0.3);color:#000}
::selection{background:rgba(255,255,0,0.3);color:#000}
a::-moz-selection{background:rgba(255,255,0,0.3);color:#0645ad}
a::selection{background:rgba(255,255,0,0.3);color:#0645ad}
p{
margin:1em 0;
}
img{
max-width:100%;
}
h1,h2,h3,h4,h5,h6{
font-weight:normal;
color:#111;
line-height:1em;
}
h4,h5,h6{ font-weight: bold; }
h1{ font-size:2.5em; }
h2{ font-size:2em; }
h3{ font-size:1.5em; }
h4{ font-size:1.2em; }
h5{ font-size:1em; }
h6{ font-size:0.9em; }
blockquote{
color:#666666;
margin:0;
padding-left: 3em;
border-left: 0.5em #EEE solid;
}
hr { display: block; height: 2px; border: 0; border-top: 1px solid #aaa;border-bottom: 1px solid #eee; margin: 1em 0; padding: 0; }
pre, code, kbd, samp { color: #000; font-family: monospace, monospace; _font-family: 'courier new', monospace; font-size: 0.98em; }
pre { white-space: pre; white-space: pre-wrap; word-wrap: break-word; }
b, strong { font-weight: bold; }
dfn { font-style: italic; }
ins { background: #ff9; color: #000; text-decoration: none; }
mark { background: #ff0; color: #000; font-style: italic; font-weight: bold; }
sub, sup { font-size: 75%; line-height: 0; position: relative; vertical-align: baseline; }
sup { top: -0.5em; }
sub { bottom: -0.25em; }
ul, ol { margin: 1em 0; padding: 0 0 0 2em; }
li p:last-child { margin:0.8em }
dd { margin: 0 0 0 2em; }
img { border: 0; -ms-interpolation-mode: bicubic; vertical-align: middle; }
table { border-collapse: collapse; border-spacing: 0; }
td { vertical-align: top; }
@media only screen and (min-width: 480px) {
body{font-size:14px;}
}
@media only screen and (min-width: 768px) {
body{font-size:16px;}
}
@media print {
* { background: transparent !important; color: black !important; filter:none !important; -ms-filter: none !important; }
body{font-size:12pt; max-width:100%;}
a, a:visited { text-decoration: underline; }
hr { height: 1px; border:0; border-bottom:1px solid black; }
a[href]:after { content: " (" attr(href) ")"; }
abbr[title]:after { content: " (" attr(title) ")"; }
.ir a:after, a[href^="javascript:"]:after, a[href^="#"]:after { content: ""; }
pre, blockquote { border: 1px solid #999; padding-right: 1em; page-break-inside: avoid; }
tr, img { page-break-inside: avoid; }
img { max-width: 100% !important; }
@page :left { margin: 15mm 20mm 15mm 10mm; }
@page :right { margin: 15mm 10mm 15mm 20mm; }
p, h2, h3 { orphans: 3; widows: 3; }
h2, h3 { page-break-after: avoid; }
}
</style>
<!--[if lt IE 9]>
<script src="//cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.3/html5shiv-printshiv.min.js"></script>
<![endif]-->
</head>
<body>
<header id="title-block-header">
<h1 class="title">Yuanbin Wu
<img style="float: right;" height="50" width="25" src="data:image/jpeg;base64,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" /></h1>
</header>
<p>I’m currently an assistant professor at <a href="http://www.cs.ecnu.edu.cn">Computer Science Department</a>, <a href="http://www.ecnu.edu.cn">East China Normal University</a>. Before
joining ECNU, I got both my B.S. (2007) and Ph.D (2012) degree from <a href="http://www.fudan.edu.cn">Fudan University</a>, and worked as a
research fellow at <a href="http://www.nus.edu.sg">National University
of Singapore</a> (2013).</p>
<h2 id="research">Research</h2>
<p>My research interests are mainly on natural language processing and
machine learning (structured prediction, online learning). I aim to
build efficient and effective models for computing human languages.</p>
<h2 id="teaching">Teaching</h2>
<ul>
<li>Advanced Machine Learning<br />
15-Fall, 14-Spring</li>
<li>Operating System Labs<br />
<a href="https://github.com/ecnu-oslab/ecnu-oslab-up/blob/main/22-Fall/index.md">22-Fall</a><a href="https://hub.fastgit.org/ecnu-oslab/ecnu-oslab-up/blob/main/22-Fall/index.md">(mirror)</a>,
<a href="https://github.com/ecnu-oslab/ecnu-oslab-up/blob/main/21-Fall/index.md">21-Fall</a><a href="https://hub.fastgit.org/ecnu-oslab/ecnu-oslab-up/blob/main/21-Fall/index.md">(mirror)</a>,
<a href="http://ybwu.org/ecnu-oslabs/index.html">20-Fall</a>, <a href="http://ybwu.org/ecnu-oslabs/19-Fall/index.html">19-Fall</a>, <a href="http://ybwu.org/ecnu-oslabs/18-Fall/index.html">18-Fall</a>, <a href="http://ybwu.org/ecnu-oslabs/17-Fall/index.html">17-Fall</a>, <a href="http://ybwu.org/ecnu-oslabs/16-Fall/index.html">16-Fall</a>, <a href="http://ybwu.org/ecnu-oslabs/15-Fall/index.html">15-Fall</a></li>
<li>Object Oriented Programming with Java<br />
<a href="http://ybwu.org/ecnu-java/index.html">22-Spring</a>, <a href="http://ybwu.org/ecnu-java/21-Spring/index.html">21-Spring</a>, <a href="http://ybwu.org/ecnu-java/20-Fall/index.html">20-Fall</a>, <a href="http://ybwu.org/ecnu-java/19-Fall/index.html">19-Fall</a>, <a href="http://ybwu.org/ecnu-java/18-Spring/index.html">18-Spring</a>, <a href="http://ybwu.org/ecnu-java/17-Spring/index.html">17-Spring</a>, <a href="http://ybwu.org/ecnu-java/16-Spring/index.html">16-Spring</a>,</li>
</ul>
<h2 id="software">Software</h2>
<p>Coming Soon</p>
<h2 id="publication">Publication</h2>
<ul>
<li><p>Extracting Entities and Relations with Joint Minimum Risk
Training<br />
Changzhi Sun, Yuanbin Wu, Man Lan, Shiliang Sun, Wenting Wang,
Kuang-Chih Lee, Kewen Wu, EMNLP 2018</p></li>
<li><p>An Adversarial Joint Learning Model for Low-Resource Language
Semantic Textual Similarity<br />
Junfeng Tian, Man Lan, Yuanbin Wu, Jingang Wang, Long Qiu, Sheng Li, Jun
Lang, Luo Si, ECIR 2018</p></li>
<li><p>Inference on Syntactic and Semantic Structures for Machine
Comprehension<br />
Chenrui Li, Yuanbin Wu, Man Lan, AAAI 2018</p></li>
<li><p>A Learning Error Analysis for Structured Prediction with
Approximate Inference<br />
Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang, NIPS
2017</p></li>
<li><p>A Fast and Lightweight System for Multilingual Dependency
Parsing<br />
Tao Ji, Yuanbin Wu, Man Lan, CoNLL Shared Task 2017</p></li>
<li><p>Large-scale Opinion Relation Extraction with Distantly Supervised
Neural Network<br />
Changzhi Sun, Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, EACL
2017</p></li>
<li><p>Multi-task Attention-based Neural Networks for Implicit Discourse
Relationship Representation and Identification<br />
Man Lan, Jianxiang Wang, Yuanbin Wu, Zheng-Yu Niu, Haifeng Wang, EMNLP
2017</p></li>
<li><p>An Online Learning Algorithm for Bilinear Models<br />
Yuanbin Wu, Shiliang Sun, ICML 2015</p></li>
<li><p>A survey of multi-source domain adaptation<br />
Shiliang Sun, Honglei Shi, Yuanbin Wu, Information Fusion 24,
2015</p></li>
<li><p>Grammatical Error Correction Using Integer Linear
Programming<br />
Yuanbin Wu, Hwee Tou Ng, ACL 2013</p></li>
<li><p>The CoNLL-2013 Shared Task on Grammatical Error Correction<br />
Hwee Tou Ng, Siew Mei Wu, Yuanbin Wu, Christian Hadiwinoto, Joel R.
Tetreault, CoNLL Shared Task, 2013</p></li>
<li><p>Structural Opinion Mining for Graph-based Sentiment
Representation<br />
Yuanbin Wu, Qi Zhang, Xuanjing Huang, Lide Wu, EMNLP 2011</p></li>
<li><p>Opinion Mining with Sentiment Graph<br />
Qi Zhang, Yuanbin Wu, Yan Wu, Xuanjing Huang, WI 2011</p></li>
<li><p>A Survey on Sentiment Analysis<br />
Xuanjing Huang, Qi Zhang, Yuanbin Wu, Journal of Chinese Information
Processing, (in Chinese) (6) 2011</p></li>
<li><p>Pseudo-Relevance Feedback Based on mRMR Criteria<br />
Yuanbin Wu, Qi Zhang, Yaqian Zhou, Xuanjing Huang, AIRS 2010</p></li>
<li><p>Phrase Dependency Parsing for Opinion Mining<br />
Yuanbin Wu, Qi Zhang, Xuanjing Huang, Lide Wu, EMNLP 2009</p></li>
<li><p>Mining product reviews based on shallow dependency parsing<br />
Qi Zhang, Yuanbin Wu, Tao Li, Mitsunori Ogihara, Joseph Johnson,
Xuanjing Huang, SIGIR (poster) 2009</p></li>
</ul>
<h2 id="contact">Contact</h2>
<p>ybwu[at]cs.ecnu.edu.cn<br />
B911 Science Building, 3663 North Zhongshan Road, Shanghai, 200062</p>
</body>
</html>