Difference between revisions of "User:Dwijaya"
From Cohen Courses
Jump to navigationJump to search| (44 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
| + | ==About== | ||
Name: Derry Tanti Wijaya | Name: Derry Tanti Wijaya | ||
| + | |||
| + | [[File:Wijaya_Derry.jpg]] | ||
Homepage: http://www.cs.cmu.edu/~dwijaya | Homepage: http://www.cs.cmu.edu/~dwijaya | ||
| − | I am a PhD student at [http://www.lti.cs.cmu.edu/ Language Technologies Institute] | + | I am a 2nd year PhD student at [http://www.lti.cs.cmu.edu/ Language Technologies Institute]. |
My research interest is in the area of information retrieval, machine learning, and natural language processing. I am currently involved in the Read The Web project that builds a system called [http://rtw.ml.cmu.edu/rtw/ NELL] which tries to learn overtime to 'read' (automatically extract facts and build a Knowledge Base of these facts) from the Web. Previously I was involved in the [http://www.e-lico.eu/ e-LICO project], specifically in the building of an ontology of data mining algorithms and models. I have also worked previously on the area of opinion mining and graph clustering. | My research interest is in the area of information retrieval, machine learning, and natural language processing. I am currently involved in the Read The Web project that builds a system called [http://rtw.ml.cmu.edu/rtw/ NELL] which tries to learn overtime to 'read' (automatically extract facts and build a Knowledge Base of these facts) from the Web. Previously I was involved in the [http://www.e-lico.eu/ e-LICO project], specifically in the building of an ontology of data mining algorithms and models. I have also worked previously on the area of opinion mining and graph clustering. | ||
| + | ==Project== | ||
| − | + | [[MaltCourses:Tweet|Inferring Geographical Activity of Users from Tweets]] (Finding out who you are from where, when, what and with whom you tweet) | |
| − | |||
| − | |||
| − | + | ==Wiki writeups== | |
| − | * [ | + | <h4>Theme: Temporal Information Extraction</h4> |
| − | * [ | + | ===Problem=== |
| − | * [ | + | * [[Temporal_ordering|Temporal ordering]] (problem) (September) |
| + | * [[Temporal_information_extraction|Temporal Information Extraction]] (problem) (October) | ||
| + | * [[Narrative_event_chains|Narrative event chains]] (problem) (November) | ||
| + | ===Dataset=== | ||
| + | * [[TimeBank_Corpus|TimeBank Corpus]] (dataset) (September) | ||
| + | * [[Gigaword_corpus|Gigaword Corpus]] (dataset) (November) | ||
| + | ===Method=== | ||
| + | * [[Integer_Linear_Programming|Integer Linear Programming]] (method) (September) | ||
| + | * [[Topics_over_Time | Topics over Time]] (method) '''new!''' (November) | ||
| + | ===Paper=== | ||
| + | * [[Chambers_and_Jurafsky,_Jointly_combining_implicit_constraints_improves_temporal_ordering,_EMNLP_2008|Chambers and Jurafsky, Jointly combining implicit constraints improves temporal ordering, EMNLP 2008]] (paper) (September) | ||
| + | * [[Yoshikawa_2009_jointly_identifying_temporal_relations_with_markov_logic|Yoshikawa et. al., Jointly Identifying Temporal Relations with Markov Logic, Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, 2009]] (paper) (September) | ||
| + | * [[Denis_and_Muller,_Predicting_Globally-Coherent_Temporal_Structures_from_Texts_via_Endpoint_Inference_and_Graph_Decomposition,_IJCAI_2011|Denis and Muller, Predicting Globally-Coherent Temporal Structures from Texts via Endpoint Inference and Graph Decomposition, IJCAI 2011]] (paper) (September) | ||
| + | * [[Das_Sarma_et._al.,_Dynamic_Relationship_and_Event_Discovery,_WSDM_2011|Das Sarma et. al., Dynamic Relationship and Event Discovery, WSDM 2011]] (paper) (October) | ||
| + | * [[Ling,_X._and_Weld,_D._Temporal_Information_Extraction._AAAI-10|Ling, X. and Weld, D. Temporal Information Extraction. AAAI-10]] (paper) (October) | ||
| + | *[[Chambers_and_Jurafsky,_Unsupervised_Learning_of_Narrative_Event_Chains,_ACL_2008|Chambers and Jurafsky, Unsupervised Learning of Narrative Event Chains, ACL 2008]] (paper) '''new!''' (November) | ||
Latest revision as of 20:35, 30 November 2011
Contents
About
Name: Derry Tanti Wijaya
Homepage: http://www.cs.cmu.edu/~dwijaya
I am a 2nd year PhD student at Language Technologies Institute.
My research interest is in the area of information retrieval, machine learning, and natural language processing. I am currently involved in the Read The Web project that builds a system called NELL which tries to learn overtime to 'read' (automatically extract facts and build a Knowledge Base of these facts) from the Web. Previously I was involved in the e-LICO project, specifically in the building of an ontology of data mining algorithms and models. I have also worked previously on the area of opinion mining and graph clustering.
Project
Inferring Geographical Activity of Users from Tweets (Finding out who you are from where, when, what and with whom you tweet)
Wiki writeups
Theme: Temporal Information Extraction
Problem
- Temporal ordering (problem) (September)
- Temporal Information Extraction (problem) (October)
- Narrative event chains (problem) (November)
Dataset
- TimeBank Corpus (dataset) (September)
- Gigaword Corpus (dataset) (November)
Method
- Integer Linear Programming (method) (September)
- Topics over Time (method) new! (November)
Paper
- Chambers and Jurafsky, Jointly combining implicit constraints improves temporal ordering, EMNLP 2008 (paper) (September)
- Yoshikawa et. al., Jointly Identifying Temporal Relations with Markov Logic, Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, 2009 (paper) (September)
- Denis and Muller, Predicting Globally-Coherent Temporal Structures from Texts via Endpoint Inference and Graph Decomposition, IJCAI 2011 (paper) (September)
- Das Sarma et. al., Dynamic Relationship and Event Discovery, WSDM 2011 (paper) (October)
- Ling, X. and Weld, D. Temporal Information Extraction. AAAI-10 (paper) (October)
- Chambers and Jurafsky, Unsupervised Learning of Narrative Event Chains, ACL 2008 (paper) new! (November)
