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Alan Ritter

Computer Science and Engineering. The Ohio State University. Office: 595 Dreese Lab. Email: ritter.1492@osu.edu. Google Scholar: https:/ scholar.google.com/citations? Semantic Scholar: https:/ www.semanticscholar.org/author/Alan-Ritter/2268403. Much of human knowledge is encoded in text. Broadly I am interested in getting computers to better understand natural language. My research uses machine learning to extract knowledge from large heterogeneous text corpora, for example Twitter. Here is a demo. Funde...

http://aritter.github.io/

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Alan Ritter | aritter.github.io Reviews
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Computer Science and Engineering. The Ohio State University. Office: 595 Dreese Lab. Email: ritter.1492@osu.edu. Google Scholar: https:/ scholar.google.com/citations? Semantic Scholar: https:/ www.semanticscholar.org/author/Alan-Ritter/2268403. Much of human knowledge is encoded in text. Broadly I am interested in getting computers to better understand natural language. My research uses machine learning to extract knowledge from large heterogeneous text corpora, for example Twitter. Here is a demo. Funde...
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1 alan ritter
2 publications
3 software
4 teaching
5 assistant professor
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7 follow @alan ritter
8 and the web
9 at carnegie mellon
10 postdoc position available
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Alan Ritter | aritter.github.io Reviews

https://aritter.github.io

Computer Science and Engineering. The Ohio State University. Office: 595 Dreese Lab. Email: ritter.1492@osu.edu. Google Scholar: https:/ scholar.google.com/citations? Semantic Scholar: https:/ www.semanticscholar.org/author/Alan-Ritter/2268403. Much of human knowledge is encoded in text. Broadly I am interested in getting computers to better understand natural language. My research uses machine learning to extract knowledge from large heterogeneous text corpora, for example Twitter. Here is a demo. Funde...

INTERNAL PAGES

aritter.github.io aritter.github.io
1

Alan Ritter

http://aritter.github.io/publications.html

Learning to Extract Events from Knowledge Base Revisions. Alexander Konovalov, Benjamin Strauss, Alan Ritter and Brendan O'Connor. Proceedings of WWW 2017. A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter. Jeniya Tabassum, Alan Ritter, Wei Xu. Proceedings of EMNLP 2016. Deep Reinforcement Learning for Dialogue Generation. Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky. Proceedings of EMNLP 2016. Language Resources and Evaluation 2016.

2

CSE 5539: Web Information Extraction

http://aritter.github.io/courses/5539_fall15.html

CSE 5539: Web Information Extraction. The course will involve reading and discussing recent papers from top conferences in the field. Students will propose and complete an open-ended course project; example projects might include anything from extracting relations from HTML tables on the web to event extraction from news or social media text. Previous experience with natural language processing and/or machine learning will be very helpful. Research groups at other universities: CMU. Each student will als...

3

Alan Ritter

http://aritter.github.io/software.html

Twitter Calendar Extraction Demo. Cybersecurity Event Extraction from Social Media Demo. Missing Data in Distant Supervision. Data-Driven Response Generation Demo.

4

Alan Ritter

http://aritter.github.io/teaching.html

CSE 5525: Speech and Language Processing. CSE 5539: Web Information Extraction. CSE 5522: Survey of Artificial Intelligence II: Advanced Techniques. CSE 5539: NLP and IE for the Social Web. Tutorial on Latent Variable Models for Lexical Semantics.

5

CSE 5522: Survey of Artificial Intelligence II: Advanced Techniques

http://aritter.github.io/courses/5522.html

CSE 5522: Survey of Artificial Intelligence II: Advanced Techniques. This course provides an overview of modern statistical AI, including probability and statistics, graphical models, and machine learning. The course also includes applications to computer vision, natural language processing, information retrieval, and speech processing. Instructor: Alan Ritter (ritter.1492@osu.edu). Time: Tuesday, Thursday 11:10 - 12:30. Place: Dreese Lab 305. Office Hours: Wednesdays 3:00-4:00pm, Dreese 595. The homewor...

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cllt.osu.edu cllt.osu.edu

Faculty and Students in CLLT at OSU

http://cllt.osu.edu/people.html

Faculty and Students in CLLT at OSU. There are currently five CLLT faculty in Linguistics and Computer Science and Engineering, as well as several other faculty members in related fields and/or departments. CLLT faculty meet often to work on joint projects, instruct and advise students, and coordinate the overall CLLT research focus. Below is a somewhat current listing of CLLT faculty, PhD students and recent graduates. Current CL Faculty and Students. Faculty in Related Fields. Advisors: Eric Fosler-Lus...

rtw.ml.cmu.edu rtw.ml.cmu.edu

Read the Web :: People

http://rtw.ml.cmu.edu/rtw/people

Research Project at Carnegie Mellon University. Research team celebrating NELL's first birthday, January 2011. The best way to get in touch with feedback/comments regarding NELL and the Read the Web project is to join our email list and discussion group. We gratefully acknowledge the contributions of many collaborators and funders to the Read the Web effort. Financial support for our research has been provided in part by DARPA. And the Brazilian agency CNPq. We thank Google and Bing. Estevam Hruschka, Jr.

course.duruofei.com course.duruofei.com

CMSC 773 Computational Linguistic II – Course | Ruofei Du

http://course.duruofei.com/cmsc773

Skip to Main Content. CMSC 773 Computational Linguistic II. Official Course Webpage: http:/ www.umiacs.umd.edu/ resnik/ling773 sp2015/. Resnik AT umd.edu). Raul Guerra (rguerra AT cs.umd.edu). See the schedule of topics. Is an awesome teacher! He gives 72% A. Due to the confidential problem, I have put my homework on a separate page here. The course was well-structured:. Course administrivia, semester plan; some statistical NLP fundamentals. Words and lexical association. More on EM and HMMs. Schwartz Ch...

noisy-text.github.io noisy-text.github.io

Named Entity Recognition in Twitter

http://noisy-text.github.io/2016/ner-shared-task.html

Named Entity Recognition in Twitter. Named entity recognition is one of the first steps in most IE pipelines. The diverse and noisy style of user-generated social media text presents serious challenges, however. Performance still lags far behind that on formal text genres such as newswire. The goal of this shared evaluation is to promote research on NER in noisy text and also help to provide a standardized dataset and methodology for evaluation. The shared competition is completed. Data available here.

cse.osu.edu cse.osu.edu

| Computer Science and Engineering

https://cse.osu.edu/directory

Skip to main content. Ohio State nav bar. Skip to main content. The Ohio State University. Computer Science and Engineering. The Ohio State University College of Engineering. Faculty and Staff Resources. Fellowship and Financial Aid. Auditory, Speech and Language Processing. Machine Learning Algorithms and Theory. Games and Virtual Environment. Networking and Distributed Computing. Software Engineering and Programming Languages. Data Management and Mining. Metric Embedding and Metric Geometry. 100;o...

turing.cs.washington.edu turing.cs.washington.edu

Turing Center at University of Washington - People

http://turing.cs.washington.edu/people.htm

Investigating problems at the crossroads of natural language processing, data mining, Web search, and the Semantic Web. Brown University, 2013). Carnegie Mellon University, 2013). Dr Tom Lin (Microsoft, 2012). Dr Stefan Schoenmackers (Decide, 2011). Dr Fei Wu (Google, 2010). Dr Michele Banko (Microsoft, 2009). University of Michigan, 2009). Northwestern University, 2008). Michael Skinner (Google, 2008). Kobi Reiter (Google, 2007). Temple University, 2007).

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Alan Ritter

Computer Science and Engineering. The Ohio State University. Office: 595 Dreese Lab. Email: ritter.1492@osu.edu. Google Scholar: https:/ scholar.google.com/citations? Semantic Scholar: https:/ www.semanticscholar.org/author/Alan-Ritter/2268403. Much of human knowledge is encoded in text. Broadly I am interested in getting computers to better understand natural language. My research uses machine learning to extract knowledge from large heterogeneous text corpora, for example Twitter. Here is a demo. Funde...

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