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Faculty of Mathematics and Computer Science Institute of Computer Science Data Science Chair Lectures Archiv 2016 WS Seminar: Ausgewählte Themen des Web 2.0
Seminar: Ausgewählte Themen des Web 2.0
Seminar: Ausgewählte Themen des Web 2.0
Veranstalter: Prof. Dr. Andreas Hotho
Alexander Dallmann, Daniel Schlör und Daniel Zoller .
Bei Fragen, Reservierung eines Themas, … wenden Sie sich bitte an Daniel Zoller .
WueCampus-Kurs: zum Kurs (Zugang wird nach der Vorbesprechung individuell freigeschaltet)
BibSonomy Gruppe: uw_ws16_web2.0
Vorbesprechung: 24.10.2016, 15:30 Uhr in B002 im Informatikgebäude
Themenvergabe und Vorbesprechung
Themen können schon vor dem Vorbesprechungstermin reserviert werden. Die Themen werden einige Zeit vor der Vorbesprechung hier auf dieser Seite bekannt gegeben.
Bei der Vorbesprechung  werden die Themen - so nicht schon vergeben - an die anwesenden Seminarteilnehmer verteilt. Grundsätzlich besteht Anwesenheitspflicht. Teilnehmer, die bereits ein Thema bearbeiten, aber ohne Absprache fehlen, verlieren den Anspruch auf ihr Thema . In der Vorbesprechung werden auch die Termine für die Vorträge festgelegt.
Konzept und Ablauf des Seminars
Im Zentrum dieses Seminars steht ein wissenschaftlicher Artikel und ein darin verwendetes Konzept . Ein Beispiel wäre das Paper  HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web
Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus In dem folgendes Konzept eine zentrale Rolle spielt:  Bayes Factor  Im Rahmen des Seminars soll nun das Konzept aufgearbeitet und verwandte Literatur gesucht werden . Das Ziel ist es, in Ausarbeitung und dem Vortrag des Seminars das gegebene Konzept unter Verwendung entsprechender Referenzen verständlich einzuführen und zu erklären . Außerdem sollen Anwendungsbeispiele oder Erweiterungen des Konzepts aus drei anderen Artikeln beleuchtet werden (dies kann den bereits gegebenen Artikel mit einschliessen).
Der Ablauf des Seminar ist wie folgt: Zuerst wird die Ausarbeitung angefertigt.  Dann wird die Arbeit abgegeben und begutachtet. Diese Begutachtung erfolgt durch den Betreuer und anonym durch zwei andere Seminarteilnehmer. Das heisst, jeder Seminarteilnehmer, muss je zwei Beurteilungen für andere Seminarteilnehmer anfertigen . Die Begutachtungen gehen zurück an den Author, der dann die Möglichkeit hat, seine Ausarbeitung zu überarbeiten.  Dann wird zeitgleich der Vortrag  (20 Minuten Vortrag, 5 Minuten Fragen) und eine einseitige Zusammenfassung vorbereitet. Der Vortrag wird dann in Blockveranstaltungen gehalten. Die oben genannten Materialien (Ausarbeitung, Vortrag, Kurzzusammenfassung) müssen vor der ersten Blockveranstaltung abgegeben werden. Bei den Blackveranstaltungen besteht Anwesenheitspflicht.
Themen
Die Gundlage zu den Vorträgen bilden wissenschaftliche Aufsätze (Papers). Jedes hier aufgeführte Paper entspricht einem Thema. themenrelevante Paper sind mit dem Tag thema versehen
die einzelnen Themen werden anhand der Tags thema:x , thema:y , etc. unterschieden
Ziel des Seminars ist es NICHT den Inhalt des Papers zusammenzufassen, sondern das Thema, das durch das Tag thema:x gegeben ist, aufzuarbeiten.
die Tags findet man auf BibSonomy in der entsprechenden Gruppe:  uw_ws16_web2.0
Lexical Coherence Graph Modeling Using Word Embeddings. Mesgar, Mohsen; Strube, Michael in HLT-NAACL , K. Knight, A. Nenkova, O. Rambow (Hrsg.) (2016). 1414–1423.
[ BibTeX ]
[ URL ]
@inproceedings{conf/naacl/MesgarS16,
author = {Mesgar, Mohsen and Strube, Michael},
booktitle = {HLT-NAACL},
crossref = {conf/naacl/2016},
editor = {Knight, Kevin and Nenkova, Ani and Rambow, Owen},
keywords = {thema:textcoherence},
pages = {1414-1423},
publisher = {The Association for Computational Linguistics},
title = {Lexical Coherence Graph Modeling Using Word Embeddings.},
year = 2016
}
Neural machine translation by jointly learning to align and translate Bahdanau, Dzmitry; Cho, Kyunghyun; Bengio, Yoshua in arXiv preprint arXiv:1409.0473 (2014).
[ Abstract ]
[ BibTeX ]
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.
@article{bahdanau2014neural,
abstract = {Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.},
author = {Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},
journal = {arXiv preprint arXiv:1409.0473},
keywords = {thema:attention},
title = {Neural machine translation by jointly learning to align and translate},
year = 2014
}
A Neuroevolution Approach to General Atari Game Playing Hausknecht, Matthew; Lehman, Joel; Miikkulainen, Risto; Stone, Peter in IEEE Trans. Comput. Intell. AI Games (2014). 6 (4) 355–366.
[ BibTeX ]
[ URL ]
@article{Hausknecht_2014,
author = {Hausknecht, Matthew and Lehman, Joel and Miikkulainen, Risto and Stone, Peter},
journal = {{IEEE} Trans. Comput. Intell. {AI} Games},
keywords = {thema:neuroevolution},
month = {dec},
number = 4,
pages = {355–366},
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
title = {A Neuroevolution Approach to General Atari Game Playing},
volume = 6,
year = 2014
}
Playing Atari with Deep Reinforcement Learning Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Graves, Alex; Antonoglou, Ioannis; Wierstra, Daan; Riedmiller, Martin in arXiv preprint arXiv:1312.5602 (2013).
[ Abstract ]
[ BibTeX ]
[ URL ]
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
@article{mnih2013playing,
abstract = {We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.},
author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Graves, Alex and Antonoglou, Ioannis and Wierstra, Daan and Riedmiller, Martin},
journal = {arXiv preprint arXiv:1312.5602},
keywords = {thema:deep_reinforcement_learning},
title = {Playing Atari with Deep Reinforcement Learning},
year = 2013
}
On optimization methods for deep learning. Le, Quoc V.; Ngiam, Jiquan; Coates, Adam; Lahiri, Ahbik; Prochnow, Bobby; Ng, Andrew Y. in ICML , L. Getoor, T. Scheffer (Hrsg.) (2011). 265–272.
[ Abstract ]
[ BibTeX ]
[ URL ]
The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are diffi- cult to tune and parallelize. These problems make it challenging to develop, debug and scale up deep learning algorithms with SGDs. In this paper, we show that more sophisti- cated off-the-shelf optimization methods such as Limited memory BFGS (L-BFGS) and Conjugate gradient (CG) with line search can significantly simplify and speed up the process of pretraining deep algorithms. In our experiments, the difference between L- BFGS/CG and SGDs are more pronounced if we consider algorithmic extensions (e.g., sparsity regularization) and hardware ex- tensions (e.g., GPUs or computer clusters). Our experiments with distributed optimiza- tion support the use of L-BFGS with locally connected networks and convolutional neural networks. Using L-BFGS, our convolutional network model achieves 0.69% on the stan- dard MNIST dataset. This is a state-of-the- art result on MNIST among algorithms that do not use distortions or pretraining.
@inproceedings{conf/icml/LeNCLPN11,
abstract = {The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are diffi- cult to tune and parallelize. These problems make it challenging to develop, debug and scale up deep learning algorithms with SGDs. In this paper, we show that more sophisti- cated off-the-shelf optimization methods such as Limited memory BFGS (L-BFGS) and Conjugate gradient (CG) with line search can significantly simplify and speed up the process of pretraining deep algorithms. In our experiments, the difference between L- BFGS/CG and SGDs are more pronounced if we consider algorithmic extensions (e.g., sparsity regularization) and hardware ex- tensions (e.g., GPUs or computer clusters). Our experiments with distributed optimiza- tion support the use of L-BFGS with locally connected networks and convolutional neural networks. Using L-BFGS, our convolutional network model achieves 0.69% on the stan- dard MNIST dataset. This is a state-of-the- art result on MNIST among algorithms that do not use distortions or pretraining.},
author = {Le, Quoc V. and Ngiam, Jiquan and Coates, Adam and Lahiri, Ahbik and Prochnow, Bobby and Ng, Andrew Y.},
booktitle = {ICML},
editor = {Getoor, Lise and Scheffer, Tobias},
keywords = {thema:optimization_algorithms},
pages = {265-272},
publisher = {Omnipress},
title = {On optimization methods for deep learning.},
year = 2011
}
Large Scale Online Learning of Image Similarity Through Ranking Chechik, Gal; Sharma, Varun; Shalit, Uri; Bengio, Samy in Journal of Machine Learning Research (2010). 11 1109–1135.
[ BibTeX ]
@article{chechik2010large,
author = {Chechik, Gal and Sharma, Varun and Shalit, Uri and Bengio, Samy},
journal = {Journal of Machine Learning Research},
keywords = {thema:similarity-learning},
pages = {1109-1135},
title = {Large Scale Online Learning of Image Similarity Through Ranking},
volume = 11,
year = 2010
}
An empirical study on learning to rank of tweets Duan, Yajuan; Jiang, Long; Qin, Tao; Zhou, Ming; Shum, Heung-Yeung in Proceedings of the 23rd International Conference on Computational Linguistics , COLING ’10 (2010). 295–303.
[ Abstract ]
[ BibTeX ]
[ URL ]
Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.
@inproceedings{Duan:2010:ESL:1873781.1873815,
abstract = {Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.},
address = {Stroudsburg, PA, USA},
author = {Duan, Yajuan and Jiang, Long and Qin, Tao and Zhou, Ming and Shum, Heung-Yeung},
booktitle = {Proceedings of the 23rd International Conference on Computational Linguistics},
keywords = {twitter},
pages = {295–303},
publisher = {Association for Computational Linguistics},
series = {COLING '10},
title = {An empirical study on learning to rank of tweets},
year = 2010
}
Accurate Information Extraction from Research Papers using Conditional Random Fields Peng, Fuchun; McCallum, Andrew (2004). 2004 329–336.
[ Abstract ]
[ BibTeX ]
With the increasing use of research paper search engines, such as CiteSeer, for both literature search and hiring decisions, the accuracy of such systems is of paramount importance. This paper employs Conditional Random Fields (CRFs) for the task of extracting various common fields from the headers and citation of research papers. The basic theory of CRFs is becoming well-understood, but best-practices for applying them to real-world data requires additional exploration. This paper makes an empirical exploration of several factors, including variations on Gaussian, exponential and hyperbolic-priors for improved regularization, and several classes of features and Markov order. On a standard benchmark data set, we achieve new state-of-the-art performance, reducing error in average F1 by 36%, and word error rate by 78% in comparison with the previous best SVM results. Accuracy compares even more favorably against HMMs.
@article{peng2004accurate,
abstract = {With the increasing use of research paper search engines, such as CiteSeer, for both literature search and hiring decisions, the accuracy of such systems is of paramount importance. This paper employs Conditional Random Fields (CRFs) for the task of extracting various common fields from the headers and citation of research papers. The basic theory of CRFs is becoming well-understood, but best-practices for applying them to real-world data requires additional exploration. This paper makes an empirical exploration of several factors, including variations on Gaussian, exponential and hyperbolic-priors for improved regularization, and several classes of features and Markov order. On a standard benchmark data set, we achieve new state-of-the-art performance, reducing error in average F1 by 36%, and word error rate by 78% in comparison with the previous best SVM results. Accuracy compares even more favorably against HMMs.},
author = {Peng, Fuchun and McCallum, Andrew},
keywords = {thema:crf},
pages = {329–336},
title = {Accurate Information Extraction from Research Papers using Conditional Random Fields},
volume = 2004,
year = 2004
}
Flexible Mixture Model for Collaborative Filtering. Si, Luo; Jin, Rong in ICML , T. Fawcett, N. Mishra (Hrsg.) (2003). 704–711.
[ BibTeX ]
[ URL ]
@inproceedings{si2003flexible,
author = {Si, Luo and Jin, Rong},
booktitle = {ICML},
crossref = {conf/icml/2003},
editor = {Fawcett, Tom and Mishra, Nina},
keywords = {thema:cf-clustering},
pages = {704-711},
publisher = {AAAI Press},
title = {Flexible Mixture Model for Collaborative Filtering.},
year = 2003
}
Leistungsnachweis
Die Ausarbeitung muss dem Betreuer zum bekanntgegebenen Termin zum Begutachten als PDF vorliegen.
Die zwei Begutachtungen anderer Ausarbeitungen, die müssen pünktlich zum bekanntgegebenen Termin angefertigt und abgegeben werden.
Ein (1!) Tag vor dem ersten geplanten Vortrag sind per Email als PDF dem Betreuer abzugeben: eine 6-seitige LaTeX-Ausarbeitung im hier  beschriebenen Format (doppelspaltig) eine einseitige Zusammenfassung (die allen Teilnehmern beim Vortrag ausgehändigt wird), der Foliensatz Vorherige Absprachen mit dem Betreuer sind ausdrücklich erwünscht. Alle verwendeten Referenzen sind zusätzlich zum Literaturverzeichnis der Ausarbeitung in  http://www.bibsonomy.org  in der entsprechenden Gruppe (relevant for) mit den folgenden Tags einzugeben: den Tags des jeweiligen Themas und weiteren sinnvollen Tags. Die Vortragsdauer beträgt verbindlich 20 Minuten, nach dem Vortrag besteht Gelegenheit zur Diskussion (ca. 5 Minuten). Der Vortrag (inkl. Folien und Abstract) geht mit 30% in die Endnote ein, die Ausarbeitung ebenfalls mit 30%. Die angefertigeten Reviews gehen 20% in die Note ein. Die letzten 20% bewerten die Eigenständigkeit der Ausarbeitung, Einhaltung von Terminen, Beteiligung am Seminar etc.
Bearbeitungshinweise
Als Richtlinie für die Erstellung einer guten Seminararbeit (inkl. Vortrag und Ausarbeitung) wird das Buch Markus Deininger and Horst Lichter and Jochen Ludewig and Kurt Schneider. Studien-Arbeiten: ein Leitfaden zur Vorbereitung, Durchführung und Betreuung von Studien-, Diplom- Abschluss- und Doktorarbeiten am Beispiel Informatik. 5. Auflage. vdf Hochschulverlag, Zürich, 2005.  empfohlen, welches beim Betreuer des Seminars (Prof. Dr. Andreas Hotho) ausliegt. Wir empfehlen die Anschaffung dieses Buchs (9,50 €), da es Sie bis zur Masterarbeit (und weiter) begleiten kann. Die Benotung der Seminararbeit erfolgt in Anlehnung an das dort auf Seite 77 angegebene Schema, angepasst auf die Erfordernisse einer Seminararbeit.
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