EMNLP 2015 読み会 @ 小町研 "Morphological Analysis for Unsegmented Languages using ...Yuki Tomo
首都大学東京 情報通信システム学域 小町研究室に行われた EMNLP 2015 読み会で "Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model" を紹介した際の資料です。
文献紹介:SemEval-2012 Task 1: English Lexical SimplificationTomoyuki Kajiwara
Lucia Specia, Sujay Kumar Jauhar, Rada Mihalcea. SemEval-2012 Task 1: English Lexical Simplification. In Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval-2012), pp.347-355, 2012.
This document summarizes context-aware recommendation and factorization machines. It discusses how factorization machines improve on traditional matrix factorization models by incorporating additional context features. It also introduces gradient boosting factorization machines which further enhance factorization machines by optimizing the factorization model with gradient boosting algorithms.
This document summarizes research on using structured event representations extracted from news articles to predict stock price movements. Key points include:
- Events are extracted from articles and represented as tuples of actors, actions, and objects to capture the who, what, when of events.
- A deep neural network model is used to predict stock price changes based on extracted event representations.
- The model achieves better performance than baselines that use bag-of-words representations of articles.
EMNLP 2015 読み会 @ 小町研 "Morphological Analysis for Unsegmented Languages using ...Yuki Tomo
首都大学東京 情報通信システム学域 小町研究室に行われた EMNLP 2015 読み会で "Morphological Analysis for Unsegmented Languages using Recurrent Neural Network Language Model" を紹介した際の資料です。
文献紹介:SemEval-2012 Task 1: English Lexical SimplificationTomoyuki Kajiwara
Lucia Specia, Sujay Kumar Jauhar, Rada Mihalcea. SemEval-2012 Task 1: English Lexical Simplification. In Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval-2012), pp.347-355, 2012.
This document summarizes context-aware recommendation and factorization machines. It discusses how factorization machines improve on traditional matrix factorization models by incorporating additional context features. It also introduces gradient boosting factorization machines which further enhance factorization machines by optimizing the factorization model with gradient boosting algorithms.
This document summarizes research on using structured event representations extracted from news articles to predict stock price movements. Key points include:
- Events are extracted from articles and represented as tuples of actors, actions, and objects to capture the who, what, when of events.
- A deep neural network model is used to predict stock price changes based on extracted event representations.
- The model achieves better performance than baselines that use bag-of-words representations of articles.
2. 概要 8.4.7でループあり確率伝搬の有用な例として紹介されている誤り訂正符号であるLDPC符号の概要について説明します 詳しく知りたい方はMackayの教科書とかを読んでください David Mackay : Information Theory, Inference, and Learning Algorithms (http://www.inference.phy.cam.ac.uk/mackay/itila/book.html, Webからpdfが無料でダウンロードできます) Frank R. Kschischang, Brendan J. Frey, Hans-Andrea Loeliger : Factor Graphs and the Sum-Product Algorithm, IEEE Trans. Inform Theory, Vol 47, No 2 , 2001 2010/02/06 PRML勉強会第9回 2
5. 通信路符号化定理 シャノンの第二基本定理とも呼ぶ Claude Shannon, "A Mathematical Theory of Communication", Bell System Technical Journal, vol. 27, pp. 379–423 and 623–656, 1948 通信レート R = n / k がXとYの相互情報量 I(X,Y)以下ならばn->∞のとき誤りのない通信が可能となる 通信路符号化定理は上を満たす符号化が存在することを保障するが構成法については述べていない 2010/02/06 PRML勉強会第9回 8
11. 最近の話 Loopy BPの代わりに線形計画法を使うことによって、より復号誤り率を低くできる Jon Feldman, Martin J. Wainwright and David R. Karger: “Using linear programming to decode binary linear codes”, IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 51, NO. 3, MARCH 2005 有歪みあり圧縮への応用 Y.Matsunaga and H.Yamamoto, "A coding theorem for lossy data compression by LDPC codes", IEEE-ISIT2002, June 30-July 5, 2002 本多, 三宅, 山本, 丸山,"LDPC符号と線形計画法を用いた情報源符号化", 信学技法, IT2008-13, pp.27-32, 2008 J.Honda, H.Yamamoto, "Variable Length Lossy Coding using an LDPC Code", IEEE-ISIT2009, pp.1973-1977, June 28- July 3, 2009 2010/02/06 PRML勉強会第9回 14