FrontPage / Learning Markov Logic
*概要 [#p5d760ff]
Statistical Relational Learning (SRL) の代表的な枠組みのひとつである Markov Logic Networks [Richardson & Domingos 2006] とその周辺技術を理解し、SRL に関する研究動向を把握する。

*記 [#pfcbebd0]
:日時|毎週水曜日 17:00-(ご飯を食べながら)
:参加者|乾,渡邉,水野,井之上,山本,岡崎

* 方法 [#c2eaf595]
- Pedro Domingos 氏の講義資料(http://homes.cs.washington.edu/~pedrod/803/)を参考に進める
- 各回の説明担当者を決めておき、講義資料またはオリジナルの説明資料を用いて発表してもらう
-- 担当者は事前に参考文献を読み、ひととおり説明できるようにしておく
- ご飯(ピザ、カレーほか)を食べながら勉強する

* 参考文献 [#u9e0655d]
 Pedro Domingos and Daniel Lowd,
 Markov Logic: An Interface Layer for AI, Morgan & Claypool, 2008.

- Video Lectures
-- [[Combining Logic and Probability: Languages, Algorithms and Applications@UAI2011>http://videolectures.net/uai2011_domingos_kersting_combining/]]
-- [[Markov Logic: A Unifying Language for Information and Knowledge Management@CIKM2008>http://videolectures.net/cikm08_domingos_mlmaul/]]
-- [[Incorporating Prior Knowledge into NLP with Markov Logic@ICML>http://videolectures.net/icml08_domingos_ipk/]]

* 担当 [#w5034d3a]
|LEFT:70||c
|日付|担当者|講義タイトル|参考文献の該当箇所|
|5/22|井之上|Introduction (&handref(2013/misc/misc-20130522-intro-naoya-i.pdf,内部資料);)|Chapter 1|
|5/22|井之上|Markov networks (&handref(2013/misc/misc-20130522-markov-naoya-i.pdf,内部資料);)|Section 2.2|
|6/5|山本|First-order logic and inductive logic programming (&handref(2013/misc/misc-20130605-kazeto.pdf,内部資料);)|Section 2.1|
|6/12|井之上|Markov logic and other SRL approaches (&handref(2013/misc/misc-20130612-naoya-i.pdf,内部資料);)|Sections 2.3 and 2.4|
|6/26|井之上|Markov logic (contd.) (&handref(2013/misc/misc-20130612-naoya-i.pdf,内部資料);)|Sections 2.3 and 2.4|
//|7/3||Applications of Markov logic|Chapter 6, Alchemy tutorial|
|7/10|水野|Inference|Chapter 3|
|7/17|水野|Inference (Lifted inference) (&handref(2013/misc/misc-20130717-junta-m-lifted.pdf,内部資料);) |Chapter 3|
//|7/17||Inference (contd.)|Chapter 3|
//|7/17|岡崎|Weight learning|Section 4.1| ... 朝日新聞取材のため、7/24 以降に移動
|7/25 13:00-|岡崎|Weight learning &handref(2013/misc/misc-20130725-okazaki.pdf,内部資料);|Section 4.1|
//|7/17||Applications of Markov logic (contd.)|Chapter 6, Alchemy tutorial|
//|7/24||Inference|Chapter 3|
//|||Inference (contd.)|Chapter 3|
//|7/29 13:00-|井之上|Structure learning|Sections 4.2, 4.3 and 4.4| ... OC、DN社内進捗等により準備間に合わず、延期
|8月末?|井之上|Structure learning|Sections 4.2, 4.3 and 4.4|

*参考リンク(ソフトウェア) [#ab67bd5a]
- Alchemy ([[http://alchemy.cs.washington.edu/]])
- Alchemy 2.0 ([[https://code.google.com/p/alchemy-2/]])
- Tuffy ([[http://hazy.cs.wisc.edu/hazy/tuffy/]])
- markov thebeast ([[https://code.google.com/p/thebeast/]])
- rockit ([[https://code.google.com/p/rockit/]])
- Walksat ([[http://www.cs.rochester.edu/u/kautz/walksat/]])

*参考リンク(論文) [#ab67bd5a]
- Application to discourse processing
-- Yufang Hou  and Katja Markert and Michael Strube. Global Inference for Bridging Anaphora Resolution. NAACL-HLT2013. [[pdf>http://aclweb.org/anthology/N/N13/N13-1111.pdf]]
-- Islam Beltagy and Cuong Chau and Gemma Boleda and Dan  Garrette and Katrin Erk and Raymond Mooney. Montague Meets Markov: Deep Semantics with Probabilistic Logical Form. *SEM2013. [[pdf>http://www.cs.utexas.edu/~ai-lab/pub-view.php?PubID=127277]]
-- Katsumasa Yoshikawa, Masayuki Asahara, Yuji Matsumoto. Jointly Extracting Japanese Predicate-Argument Relation with Markov Logic. IJCNLP2011. [[pdf>http://aclweb.org/anthology-new/I/I11/I11-1126.pdf]]
-- James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate and Raymond J. Mooney. Implementing Weighted Abduction in Markov Logic. IWCS2011. [[pdf>http://www.cs.utexas.edu/users/ml/papers/blythe.iwcs11.pdf]]
-- Dan Garrette, Katrin Erk, Raymond Mooney. Integrating Logical Representations with Probabilistic Information using Markov Logic. IWCS2011. [[pdf>http://www.cs.utexas.edu/users/ml/papers/garrette.iwcs.11.pdf]]
-- Stefan Schoenmackers, Jesse Davis, Oren Etzioni and Daniel Weld. Learning First-Order Horn Clauses from Web Text. EMNLP2010. [[pdf>http://www.aclweb.org/anthology/D/D10/D10-1106.pdf]]
-- RohitJ. Kate RaymondJ. Mooney. Probabilistic Abduction using Markov Logic Networks. IJCAI 2009 on PAIR 2009. [[pdf>http://www.cs.utexas.edu/users/ml/papers/kate-pair09.pdf]]
-- Stefan Schoenmackers, Oren Etzioni and Daniel Weld. Scaling Textual Inference to the Web. EMNLP2010. [[pdf>http://turing.cs.washington.edu/papers/Stef_emnlp08.pdf]]
-- Hoifung Poon, Pedro Domingos. Joint Unsupervised Coreference Resolution with Markov Logic. EMNLP2008. [[pdf>http://www.aclweb.org/anthology/D/D08/D08-1068.pdf]]

- Inference
-- '''Cutting Plane Aggregation''': Jan Noessner, Mathias Niepert, Heiner Stuckenschmidt. RockIt: Exploiting Parallelism and Symmetry for MAP Inference in Statistical Relational Models. AAAI2013. [[pdf>http://arxiv.org/abs/1304.4379]]
-- '''Lifted inference''': Vibhav Gogate and Pedro Domingos. Probabilistic Theorem Proving. UAI2011. [[pdf>http://homes.cs.washington.edu/~pedrod/papers/uai11b.pdf]]
-- '''Cutting Plane Inference''': Sebastian Riedel, Cutting Plane MAP Inference for Markov Logic. SRL 2009. [[pdf>http://alchemy.cs.washington.edu/papers/pdfs/riedel09.pdf]]
-- '''MC-SAT''': Hoifung Poon, Pedro Domingos. Sound and Efficient Inference with Probabilistic and Deterministic Dependencies. AAAI 2006. [[pdf>http://homes.cs.washington.edu/~pedrod/papers/aaai06a.pdf]]
-- '''LazySAT''': Parag Singla, Pedro Domingos. Memory-Efficient Inference in Relational Domains. AAAI 2006. [[pdf>http://homes.cs.washington.edu/~pedrod/papers/aaai06b.pdf]]
-- '''SampleSAT''': Wei Wei, Jordan Erenrich, and Bart Selman. Towards Efficient Sampling: Exploiting Random Walk Strategies. AAAI 2004. [[pdf>http://www.cs.cornell.edu/selman/papers/pdf/04.aaai.sampling.pdf]]
-- '''WalkSAT''': Bart Selman, Henry Kautz, and Bram Cohen. Local Search Strategies for Satisfiability Testing. 1996. [[pdf>http://www.cs.rochester.edu/u/kautz/walksat/]]

- Structure/Weight Learning
-- Tuyen N. Huynh and Raymond J. Mooney. Max-Margin Weight Learning for Markov Logic Networks. SRL2009. [[pdf>http://www.cs.utexas.edu/~ai-lab/pub-view.php?PubID=126914]]


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