WEBIS LECTURENOTES SLIDES Collection. Datenbanken Part. Einführung; Unit. Organisation, Literatur: 03.02.21: Unit. Problemstellungen, Begriffe: 03.02.21: Part. Konzeptueller Datenbankentwurf

WEBIS FOR STUDENTS

Students who are eager to develop their skills by doing a research-oriented thesis in our group should mail their interests to webis@listserv.uni-weimar.de. Suitable topic candidates are shown in the following list, which is not meant to be complete though: An Exploratory Study on

PAN @ CLEF 2021

May 20, 2021: Software submission deadline. May 28, 2021: Participant paper submission June 11, 2021: Peer review notification. July 2, 2021: Camera-ready participant papers submission. TBD: Early bird conference registration. September 21-24, 2021: Conference. CHAPTER ML:II (CONTINUED) Concept Learning: Search in Hypothesis Space Simple Classification Problems Setting: q Xis a set of feature vectors. q Cis a set of two classes: f0;1g, fyes;nog, “belongs to a concept or not” q c: X!Cis the (unknown) ideal classifier for X. q D= f(x 1;c(x 1));:::;(x n;c(x n))g X Cis a set of examples. Todo: q Approximate c(x), which is implicitly given via D, with a feature-value pattern. WEBIS DATA GENRE-KI-04 Synopsis. The web genre corpus 2004 (Genre-KI-04) is designed for the evaluation of techniques for genre classification. It consists of 1239

web documents

PAN

PAN is a series of scientific events and shared tasks on digital text forensics and stylometry.

PAN @ CLEF 2020

June 28, 2020 August 28, 2020: Camera-ready participant papers submission. Caution: There is no time for amendments. Late submission or missing data/documents will cause removal from proceedings. TBD: Early bird conference registration. September 22-25, 2020: Online

Conference.

CHAPTER ML:IV (CONTINUED) Bayes Classification Generative Approach to Classification Problems Setting: q Xis a set of feature vectors. q Cis a set of classes. q c: X!Cis the (unknown) ideal classifier for X. q D= f(x 1;c(x 1));:::;(x n;c(x n))g X Cis a set of examples. Todo: q Approximate c(x), which is implicitly given via D, by estimating the underlying joint probability P(x;c(x)). ML:IV-58 Statistical Learning

KAPITEL DB:I

Datenintensive Anwendungen Beispiel: Hotelreservierung q Hotel Reservation Service HRS – zweitgrößter Hotel-Anbieter der Welt – zweitgrößte Tourismus-Webseite Europas – basiert auf riesigen Datenbanken q Daten bei HRS (Stand 2011) – 250000+ Hotels ca. 100 Metadaten und 1000+ Amenities pro Hotel, dynamische Angebote – 30000000+ Buchungen ca. 100 Metadaten pro Buchung, 3000000 RECHNERKOMMUNIKATION UND PROTOKOLLE Kapitel WT:II II.Rechnerkommunikation und Protokolle q Rechnernetze q Prinzipien des Datenaustauschs q Netzsoftware und Kommunikationsprotokolle q Internetworking q Client-Server-Interaktionsmodell q Uniform Resource Locator q Hypertext-Transfer-Protokoll HTTP q Fortgeschrittene HTTP-Konzepte WT:II-1 Rechnerkommunikation und Protokolle ©STEIN 2021 WEBIS LECTURENOTES SLIDES Collection. Datenbanken Part. Einführung; Unit. Organisation, Literatur: 03.02.21: Unit. Problemstellungen, Begriffe: 03.02.21: Part. Konzeptueller Datenbankentwurf

WEBIS FOR STUDENTS

Students who are eager to develop their skills by doing a research-oriented thesis in our group should mail their interests to webis@listserv.uni-weimar.de. Suitable topic candidates are shown in the following list, which is not meant to be complete though: An Exploratory Study on

PAN @ CLEF 2021

May 20, 2021: Software submission deadline. May 28, 2021: Participant paper submission June 11, 2021: Peer review notification. July 2, 2021: Camera-ready participant papers submission. TBD: Early bird conference registration. September 21-24, 2021: Conference. CHAPTER ML:II (CONTINUED) Concept Learning: Search in Hypothesis Space Simple Classification Problems Setting: q Xis a set of feature vectors. q Cis a set of two classes: f0;1g, fyes;nog, “belongs to a concept or not” q c: X!Cis the (unknown) ideal classifier for X. q D= f(x 1;c(x 1));:::;(x n;c(x n))g X Cis a set of examples. Todo: q Approximate c(x), which is implicitly given via D, with a feature-value pattern. WEBIS DATA GENRE-KI-04 Synopsis. The web genre corpus 2004 (Genre-KI-04) is designed for the evaluation of techniques for genre classification. It consists of 1239

web documents

PAN

PAN is a series of scientific events and shared tasks on digital text forensics and stylometry.

PAN @ CLEF 2020

June 28, 2020 August 28, 2020: Camera-ready participant papers submission. Caution: There is no time for amendments. Late submission or missing data/documents will cause removal from proceedings. TBD: Early bird conference registration. September 22-25, 2020: Online

Conference.

CHAPTER ML:IV (CONTINUED) Bayes Classification Generative Approach to Classification Problems Setting: q Xis a set of feature vectors. q Cis a set of classes. q c: X!Cis the (unknown) ideal classifier for X. q D= f(x 1;c(x 1));:::;(x n;c(x n))g X Cis a set of examples. Todo: q Approximate c(x), which is implicitly given via D, by estimating the underlying joint probability P(x;c(x)). ML:IV-58 Statistical Learning

KAPITEL DB:I

Datenintensive Anwendungen Beispiel: Hotelreservierung q Hotel Reservation Service HRS – zweitgrößter Hotel-Anbieter der Welt – zweitgrößte Tourismus-Webseite Europas – basiert auf riesigen Datenbanken q Daten bei HRS (Stand 2011) – 250000+ Hotels ca. 100 Metadaten und 1000+ Amenities pro Hotel, dynamische Angebote – 30000000+ Buchungen ca. 100 Metadaten pro Buchung, 3000000 RECHNERKOMMUNIKATION UND PROTOKOLLE Kapitel WT:II II.Rechnerkommunikation und Protokolle q Rechnernetze q Prinzipien des Datenaustauschs q Netzsoftware und Kommunikationsprotokolle q Internetworking q Client-Server-Interaktionsmodell q Uniform Resource Locator q Hypertext-Transfer-Protokoll HTTP q Fortgeschrittene HTTP-Konzepte WT:II-1 Rechnerkommunikation und Protokolle ©STEIN 2021 MACHINE LEARNING BASICS Remarks (continued): q From a statistical viewpoint, both x and yare random variables (vectorial and scalar respectively). Each feature vector, x i, and outcome, y i, is the result of a random experiment and hence is governed by a—usually unknown—probability distribution. q The distributions of the random variables y i and (y i y(x i)) are identical. q Equation (2): Estimating w by RSS

PAN

PAN is a series of scientific events and shared tasks on digital text forensics and stylometry.

CHAPTER NLP:II

Text Statistics Term Frequency: Zipf’s Law q The distribution of word frequencies is very skewed: Few words occur very frequently, many words hardly ever. q For example, the two most common English words(the, of)make up about 10% of all word occurrences in text documents. In large text samples, about

SUMMARIES - WEBIS

Post tl;dr unified-pgn unified-vae-pgn transf-seq2seq pseudo-self-attn tldr-bottom-up; The issue isn’t about skin color, it’s about culture. Because light-skinned people have historically pushed dark-skinned people down not just in the US, or even just in European-descended cultures, look at India’s Caste system, being proud of being light skinned, especially of being a white person II.ARCHITECTURE OF A SEARCH ENGINE Acquisition Converter The converter unifies documents as follows: q Reformatting / text extraction – Documents come in a variety of formats (e.g., HTML, PDF, DOC) – Subsequent processing steps require unified input format – Extracting plain text from binary documents is lossy (e.g., formatting is lost) – Extracting text formatting is important, too, for subsequent steps MULTILINGUAL DETECTION OF FAKE NEWS SPREADERS VIA SPARSE Multilingual Detection of Fake News Spreaders via Sparse Matrix Factorization Notebook for PAN at CLEF 2020 Boško Koloski 1;2, Senja Pollak , and Blaž Škrlj1 1Jožef Stefan Institute, Ljubljana 2Faculty of Information Science - University of Ljubljana , Slovenia blaz.skrlj@ijs.si Abstract Fake news is an emerging problem in online news and social media.

PAN PUBLICATIONS

Hamed Babaei Giglou, Jafar Razmara, Mostafa Rahgouy, and Mahsa Sanaei. LSACoNet: A Combination of Lexical and Conceptual Features for Analysis of Fake News Spreaders on

PAN @ CLEF 2020

Authorship verification is the task of deciding whether two texts have been written by the same author based on comparing the texts' writing styles. In the coming three years at PAN 2020 to PAN 2022, we develop a new experimental setup that addresses three key questions in authorship verification that have not been studied at scale to date

PAN @ CLEF 2021

Task. Authorship verification is the task of deciding whether two texts have been written by the same author based on comparing the texts' writing styles. With PAN 2020, we started to develop a new experimental setup that addresses three key questions in authorship verification that have not been studied at scale to date: Year 1 (PAN

2020

PAN @ CLEF 2021

Therefore, the task for PAN'21 is to first detect whether a document was authored by one or multiple authors, for two-author documents, the task is to find the position of the authorship change and for multi-author documents, the task is to find all positions of authorship changes. Given a document, we ask participants to answer

the following

WEBIS LECTURENOTES SLIDES Collection. Datenbanken Part. Einführung; Unit. Organisation, Literatur: 03.02.21: Unit. Problemstellungen, Begriffe: 03.02.21: Part. Konzeptueller Datenbankentwurf CHAPTER ML:II (CONTINUED) Concept Learning: Search in Hypothesis Space Simple Classification Problems Setting: q Xis a set of feature vectors. q Cis a set of two classes: f0;1g, fyes;nog, “belongs to a concept or not” q c: X!Cis the (unknown) ideal classifier for X. q D= f(x 1;c(x 1));:::;(x n;c(x n))g X Cis a set of examples. Todo: q Approximate c(x), which is implicitly given via D, with a feature-value pattern.

PAN @ CLEF 2021

May 20, 2021: Software submission deadline. May 28, 2021: Participant paper submission June 11, 2021: Peer review notification. July 2, 2021: Camera-ready participant papers submission. TBD: Early bird conference registration. September 21-24, 2021: Conference.

PAN

PAN is a series of scientific events and shared tasks on digital text forensics and stylometry.

CHAPTER NLP:II

Text Statistics Term Frequency: Zipf’s Law q The distribution of word frequencies is very skewed: Few words occur very frequently, many words hardly ever. q For example, the two most common English words(the, of)make up about 10% of all word occurrences in text documents. In large text samples, about

CHAPTER ML:I

Remarks: q Example: chess – task = playing chess – performance measure = number of games won during a world championship – experience = possibility to play against itself q Example: optical character recognition – task = isolation and classification of handwritten words in bitmaps – performance measure = percentage of correctly classified words – experience = collection of

PAN @ CLEF 2020

June 28, 2020 August 28, 2020: Camera-ready participant papers submission. Caution: There is no time for amendments. Late submission or missing data/documents will cause removal from proceedings. TBD: Early bird conference registration. September 22-25, 2020: Online

Conference.

II.ARCHITECTURE OF A SEARCH ENGINE Acquisition Converter The converter unifies documents as follows: q Reformatting / text extraction – Documents come in a variety of formats (e.g., HTML, PDF, DOC) – Subsequent processing steps require unified input format – Extracting plain text from binary documents is lossy (e.g., formatting is lost) – Extracting text formatting is important, too, for subsequent steps

KUBERNETESTUTORIAL

KubernetesTutorial Michael Völske 2019 Thistutorialisusedinternallyatthewebisgroup,andassumesaccesstoourinfrastructure.

It

PAN @ CLEF 2020

WEBIS LECTURENOTES SLIDES Collection. Datenbanken Part. Einführung; Unit. Organisation, Literatur: 03.02.21: Unit. Problemstellungen, Begriffe: 03.02.21: Part. Konzeptueller Datenbankentwurf CHAPTER ML:II (CONTINUED) Concept Learning: Search in Hypothesis Space Simple Classification Problems Setting: q Xis a set of feature vectors. q Cis a set of two classes: f0;1g, fyes;nog, “belongs to a concept or not” q c: X!Cis the (unknown) ideal classifier for X. q D= f(x 1;c(x 1));:::;(x n;c(x n))g X Cis a set of examples. Todo: q Approximate c(x), which is implicitly given via D, with a feature-value pattern.

PAN @ CLEF 2021

May 20, 2021: Software submission deadline. May 28, 2021: Participant paper submission June 11, 2021: Peer review notification. July 2, 2021: Camera-ready participant papers submission. TBD: Early bird conference registration. September 21-24, 2021: Conference.

PAN

PAN is a series of scientific events and shared tasks on digital text forensics and stylometry.

CHAPTER NLP:II

Text Statistics Term Frequency: Zipf’s Law q The distribution of word frequencies is very skewed: Few words occur very frequently, many words hardly ever. q For example, the two most common English words(the, of)make up about 10% of all word occurrences in text documents. In large text samples, about

CHAPTER ML:I

Remarks: q Example: chess – task = playing chess – performance measure = number of games won during a world championship – experience = possibility to play against itself q Example: optical character recognition – task = isolation and classification of handwritten words in bitmaps – performance measure = percentage of correctly classified words – experience = collection of

PAN @ CLEF 2020

June 28, 2020 August 28, 2020: Camera-ready participant papers submission. Caution: There is no time for amendments. Late submission or missing data/documents will cause removal from proceedings. TBD: Early bird conference registration. September 22-25, 2020: Online

Conference.

II.ARCHITECTURE OF A SEARCH ENGINE Acquisition Converter The converter unifies documents as follows: q Reformatting / text extraction – Documents come in a variety of formats (e.g., HTML, PDF, DOC) – Subsequent processing steps require unified input format – Extracting plain text from binary documents is lossy (e.g., formatting is lost) – Extracting text formatting is important, too, for subsequent steps

KUBERNETESTUTORIAL

KubernetesTutorial Michael Völske 2019 Thistutorialisusedinternallyatthewebisgroup,andassumesaccesstoourinfrastructure.

It

PAN @ CLEF 2020

WEBIS DATA GENRE-KI-04 Synopsis. The web genre corpus 2004 (Genre-KI-04) is designed for the evaluation of techniques for genre classification. It consists of 1239

web documents

CHAPTER NLP:II

Text Statistics Term Frequency: Zipf’s Law q The distribution of word frequencies is very skewed: Few words occur very frequently, many words hardly ever. q For example, the two most common English words(the, of)make up about 10% of all word occurrences in text documents. In large text samples, about

WEBIS.DE

Main Leaderboard. Main leaderboard for the SemEval 2019 Task 4 on Hyperpartisan News Detection, based on the unpublished test set for labels-by-article. Maja Karasalo, Andreas Horndahl. Magnus Rosell, Fredrik Johansson (FOI, Sweden) Bozhidar Stevanoski,

CHAPTER ML:I

Remarks: q Example: chess – task = playing chess – performance measure = number of games won during a world championship – experience = possibility to play against itself q Example: optical character recognition – task = isolation and classification of handwritten words in bitmaps – performance measure = percentage of correctly classified words – experience = collection of II.ARCHITECTURE OF A SEARCH ENGINE Acquisition Converter The converter unifies documents as follows: q Reformatting / text extraction – Documents come in a variety of formats (e.g., HTML, PDF, DOC) – Subsequent processing steps require unified input format – Extracting plain text from binary documents is lossy (e.g., formatting is lost) – Extracting text formatting is important, too, for subsequent steps

PAN PUBLICATIONS

Hamed Babaei Giglou, Jafar Razmara, Mostafa Rahgouy, and Mahsa Sanaei. LSACoNet: A Combination of Lexical and Conceptual Features for Analysis of Fake News Spreaders on

DATA - PAN

Datasets used in PAN shared tasks. None of our corpora match your

filter.

PAN ORGANIZATION

Contact and Communication. Mailing list organizers: pan@webis.de. Mailing list participants: pan-workshop-series@googlegroups.com Twitter: @webis_de.

PAN @ CLEF 2020

Celebrity Profiling. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), July 2019. ACL. Matti Wiegmann, Benno Stein, Martin Potthast. Overview of the Celebrity Profiling Task at PAN 2019. CLEF 2019 Labs and Workshops, Notebook Papers, September 2019. CEUR-WS.org. NAIVE-BAYESIAN CLASSIFICATION FOR BOT DETECTION IN TWITTER Naive-Bayesian Classification for Bot Detection in Twitter Notebook for PAN at CLEF 2019 Pablo Gamallo 1and Sattam Almatarneh;2 1 Centro de Investigación en Tecnoloxías Intelixentes (CiTIUS) University of Santiago de Compostela, Galiza WEBIS LECTURENOTES SLIDES Collection. Datenbanken Part. Einführung; Unit. Organisation, Literatur: 03.02.21: Unit. Problemstellungen, Begriffe: 03.02.21: Part. Konzeptueller Datenbankentwurf CHAPTER ML:II (CONTINUED) Concept Learning: Search in Hypothesis Space Simple Classification Problems Setting: q Xis a set of feature vectors. q Cis a set of two classes: f0;1g, fyes;nog, “belongs to a concept or not” q c: X!Cis the (unknown) ideal classifier for X. q D= f(x 1;c(x 1));:::;(x n;c(x n))g X Cis a set of examples. Todo: q Approximate c(x), which is implicitly given via D, with a feature-value pattern.

PAN @ CLEF 2021

May 20, 2021: Software submission deadline. May 28, 2021: Participant paper submission June 11, 2021: Peer review notification. July 2, 2021: Camera-ready participant papers submission. TBD: Early bird conference registration. September 21-24, 2021: Conference.

PAN

PAN is a series of scientific events and shared tasks on digital text forensics and stylometry.

CHAPTER ML:I

Remarks: q Example: chess – task = playing chess – performance measure = number of games won during a world championship – experience = possibility to play against itself q Example: optical character recognition – task = isolation and classification of handwritten words in bitmaps – performance measure = percentage of correctly classified words – experience = collection of II.ARCHITECTURE OF A SEARCH ENGINE Acquisition Converter The converter unifies documents as follows: q Reformatting / text extraction – Documents come in a variety of formats (e.g., HTML, PDF, DOC) – Subsequent processing steps require unified input format – Extracting plain text from binary documents is lossy (e.g., formatting is lost) – Extracting text formatting is important, too, for subsequent steps

CHAPTER IR:VIII

Performance Measures Effectiveness and Efficiency Effectiveness is “the degree to which something is successful in producing a desired

result; success”.

PAN @ CLEF 2020

June 28, 2020 August 28, 2020: Camera-ready participant papers submission. Caution: There is no time for amendments. Late submission or missing data/documents will cause removal from proceedings. TBD: Early bird conference registration. September 22-25, 2020: Online

Conference.

KUBERNETESTUTORIAL

KubernetesTutorial Michael Völske 2019 Thistutorialisusedinternallyatthewebisgroup,andassumesaccesstoourinfrastructure.

It

PAN @ CLEF 2020

WEBIS LECTURENOTES SLIDES Collection. Datenbanken Part. Einführung; Unit. Organisation, Literatur: 03.02.21: Unit. Problemstellungen, Begriffe: 03.02.21: Part. Konzeptueller Datenbankentwurf CHAPTER ML:II (CONTINUED) Concept Learning: Search in Hypothesis Space Simple Classification Problems Setting: q Xis a set of feature vectors. q Cis a set of two classes: f0;1g, fyes;nog, “belongs to a concept or not” q c: X!Cis the (unknown) ideal classifier for X. q D= f(x 1;c(x 1));:::;(x n;c(x n))g X Cis a set of examples. Todo: q Approximate c(x), which is implicitly given via D, with a feature-value pattern.

PAN @ CLEF 2021

May 20, 2021: Software submission deadline. May 28, 2021: Participant paper submission June 11, 2021: Peer review notification. July 2, 2021: Camera-ready participant papers submission. TBD: Early bird conference registration. September 21-24, 2021: Conference.

PAN

PAN is a series of scientific events and shared tasks on digital text forensics and stylometry.

CHAPTER ML:I

Remarks: q Example: chess – task = playing chess – performance measure = number of games won during a world championship – experience = possibility to play against itself q Example: optical character recognition – task = isolation and classification of handwritten words in bitmaps – performance measure = percentage of correctly classified words – experience = collection of II.ARCHITECTURE OF A SEARCH ENGINE Acquisition Converter The converter unifies documents as follows: q Reformatting / text extraction – Documents come in a variety of formats (e.g., HTML, PDF, DOC) – Subsequent processing steps require unified input format – Extracting plain text from binary documents is lossy (e.g., formatting is lost) – Extracting text formatting is important, too, for subsequent steps

CHAPTER IR:VIII

Performance Measures Effectiveness and Efficiency Effectiveness is “the degree to which something is successful in producing a desired

result; success”.

PAN @ CLEF 2020

June 28, 2020 August 28, 2020: Camera-ready participant papers submission. Caution: There is no time for amendments. Late submission or missing data/documents will cause removal from proceedings. TBD: Early bird conference registration. September 22-25, 2020: Online

Conference.

KUBERNETESTUTORIAL

KubernetesTutorial Michael Völske 2019 Thistutorialisusedinternallyatthewebisgroup,andassumesaccesstoourinfrastructure.

It

PAN @ CLEF 2020

WEBIS DATA GENRE-KI-04 Synopsis. The web genre corpus 2004 (Genre-KI-04) is designed for the evaluation of techniques for genre classification. It consists of 1239

web documents

CHAPTER NLP:II

Text Statistics Term Frequency: Zipf’s Law q The distribution of word frequencies is very skewed: Few words occur very frequently, many words hardly ever. q For example, the two most common English words(the, of)make up about 10% of all word occurrences in text documents. In large text samples, about

CHAPTER ML:I

Remarks: q Example: chess – task = playing chess – performance measure = number of games won during a world championship – experience = possibility to play against itself q Example: optical character recognition – task = isolation and classification of handwritten words in bitmaps – performance measure = percentage of correctly classified words – experience = collection of II.ARCHITECTURE OF A SEARCH ENGINE Acquisition Converter The converter unifies documents as follows: q Reformatting / text extraction – Documents come in a variety of formats (e.g., HTML, PDF, DOC) – Subsequent processing steps require unified input format – Extracting plain text from binary documents is lossy (e.g., formatting is lost) – Extracting text formatting is important, too, for subsequent steps

CHAPTER ML:III

Remarks: q The standard k 1-simplex comprises all k-tuples with non-negative elements that sum to 1: k 1 = n (p 1;:::;p k) 2Rk: P k i=1 p i = 1 and p i 0 for all i o q Observe the different domains of the impurity function in the definitions for and (D), namely, k and D. The domains correspond to each other: the set of examples, D, defines via its class portions an element from k RECHNERKOMMUNIKATION UND PROTOKOLLE Kapitel WT:II II.Rechnerkommunikation und Protokolle q Rechnernetze q Prinzipien des Datenaustauschs q Netzsoftware und Kommunikationsprotokolle q Internetworking q Client-Server-Interaktionsmodell q Uniform Resource Locator q Hypertext-Transfer-Protokoll HTTP q Fortgeschrittene HTTP-Konzepte WT:II-1 Rechnerkommunikation und Protokolle ©STEIN 2021

DATA - PAN

Datasets used in PAN shared tasks. None of our corpora match your

filter.

LOGISCHER DATENBANKENTWURF MIT DEM RELATIONALEN MODELL Kapitel DB:III III.Logischer Datenbankentwurf mit dem relationalen Modell q Das relationale Modell q Integritätsbedingungen q Umsetzung ER-Schema in relationales Schema q Vergleichende Syntax-Übersicht DB:III-1 Logischer Datenbankentwurf mit dem relationalen Modell

�STEIN 2021

PAN @ CLEF 2021

Therefore, the task for PAN'21 is to first detect whether a document was authored by one or multiple authors, for two-author documents, the task is to find the position of the authorship change and for multi-author documents, the task is to find all positions of authorship changes. Given a document, we ask participants to answer

the following

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