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|Term=WS
|Term=WS
|Year=2018
|Year=2018
|Module=INF-BAS2, INF-VERT2, MCL-KR, MCL-AI
|SWSLecture=2
|SWSLecture=2
|SWSExercise=2
|SWSExercise=2
|SWSPractical=0
|SWSPractical=0
|Exam type=mündliche Prüfung
|Exam type=mündliche Prüfung
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, Support Vector Machines (SVM). At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.  
|Description=Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.  


==Schedule==
==Schedule==
To be announced soon and does not start before 15 October 2018.
The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.
|Literature=LITERATURE
* Friday, 9.20 -- 10.50 am (lecture), Room  APB/E005
Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful.  
* Friday, 11.10 -- 12.40 am (tutorial), Room  APB/E005 or PC-Pool APB-E065
* Monday, 14.1.2019, 11.10 --12.40 am (lecture), Room APB/E007
* Monday, 14.1.2019, 1.00 -- 2.30 pm (tutorial, Room APB/E0069
 
Lecture free periods are:
* Friday, 12 October 2018  
* Friday, 14 December 2018 (instead: Monday 14.1.2019 11.10 -- 2.30 pm; see above)
* Saturday, 22 December 2018 to Sunday 06 January 2019
 
==Materials==
====Lecture====
All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page). 
 
====Tutorial====
All materials for the tutorials can be found at this linked [https://github.com/dgromann/SemComp_WS2018 <u>Semantic Computing GitHub</u>].
 
====Platforms====
This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to [https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/18673631233 <u>THIS LINK</u>], login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.
 
==Contact==
Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2036), please also send me an [mailto:dagmar_gromann@tu-dresden.de?Subject=Semantic%20Computing%20Lecture&body=Your%20Text e-mail].
|Literature=Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.


==Resources==
==Resources==
Zeile 24: Zeile 46:
==Machine Learning==
==Machine Learning==
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
* Bishop, Christopher (2006) "Pattern Recognition and Machine Learning", Springer.
* Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
* Grus, Joel (2015) "Data Science from Scratch", O'Reilly Media  
* Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
* Goodfellow, Ian, Bengio, Yoshua and Courville, Aaron (2016) "Deep Learning" online edition available at www.deeplearningbook.org
* Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. [http://www.acad.bg/ebook/ml/MITPress-%20SemiSupervised%20Learning.pdf <u>online edition</u>]
* Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, [http://www.deeplearningbook.org <u>online edition</u>]
 
==Linguistics==
* Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, [http://www.blackwellpublishing.com/content/BPL_Images/Content_store/WWW_Content/9780631204978/15.pdf <u>online edition </u>]
* Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
* Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
* O'Grady, W.,  Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
* Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.


==Formal Semantics==
==Computational Linguistics==
* Van Eijck, J., & Unger, C. (2010). Computational semantics with functional programming. Cambridge University Press. (Good overview of formal semantics with practical examples)
*Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, [http://www.cs.colorado.edu/~martin/slp2.html <u>online 2nd edition</u>], [https://web.stanford.edu/~jurafsky/slp3/ <u>partial 3rd edition</u>]
* [https://plato.stanford.edu/entries/montague-semantics/ Montague Semantics]
* Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, [http://www.nltk.org/book <u>online edition</u>]
* Lappin, S. (2003). [https://s3.amazonaws.com/academia.edu.documents/38967787/The_Handbook_of_Linguistics_Mark_Aronoff___Janie_Rees_-Miller_2003.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&Expires=1523905031&Signature=f%2FQOm8BoQxjNMQTLv7DuT3vzSQc%3D&response-content-disposition=inline%3B%20filename%3DThe_Handbook_of_Linguistics_Mark_Aronoff.pdf#page=388 An introduction to formal semantics]. The handbook of linguistics, 369-393.
* Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
* Alama, Jesse and Korbmacher, Johannes, "The Lambda Calculus", The Stanford Encyclopedia of Philosophy (Summer 2018 Edition), Edward N. Zalta (ed.), forthcoming URL = <https://plato.stanford.edu/archives/sum2018/entries/lambda-calculus/> (Currently: <https://plato.stanford.edu/entries/lambda-calculus/>)
* Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. [https://nlp.stanford.edu/IR-book/information-retrieval-book.html <u>online edition</u>]


==Linguistics==
==Finding Conference Contributions==
* Yule, G. (2014) "The Study of language", Cambridge. 5th ed. Cambridge University Press.  
* In general: [http://scholar.google.de <u>Google Scholar</u>]
* Cruse, A. (2011). "Meaning in language: An introduction to semantics and pragmatics", Oxford University Press.
* Computational linguistic conference proceedings: [https://aclweb.org/anthology/ <u>ACL Anthology</u>]
* O'Grady, William, John Archibald, et al. (2009) "Contemporary Linguistics: An Introduction." 6th ed. Bedford / St. Martin's, 2009.
* Deep learning: [http://www.arxiv-sanity.com/ <u>Arxiv Sanity</u>]
* David Crystal (1997) "The Cambridge Encyclopedia of Language", Cambridge University Press.
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 1
|Room=APB E005
|Date=2018/10/19
|DS=DS2
|Download=Lecture-01.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 2
|Room=APB E005
|Date=2018/10/26
|DS=DS1
|Download=Lecture-02.pdf,Lecture-02.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 3
|Room=APB E005
|Date=2018/11/02
|DS=DS1
|Download=Lecture-03.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 4
|Room=APB E005
|Date=2018/11/09
|DS=DS1
|Download=Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,Lecture-04.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 5
|Room=APB E005
|Date=2018/11/16
|DS=DS1
|Download=Lecture-05.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 6
|Room=APB E005
|Date=2018/11/23
|DS=DS1
|Download=Lecture-06.pdf,Lecture-06.pdf,Lecture-06.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 7
|Room=APB E005
|Date=2018/11/30
|DS=DS1
|Download=Lecture-07.pdf,Lecture-07.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 8
|Room=APB E005
|Date=2018/12/06
|DS=DS1
|Download=Lecture-08.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 9
|Room=APB E005
|Date=2018/12/20
|DS=DS1
|Download=Lecture-09.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 10
|Room=APB E005
|Date=2019/01/11
|DS=DS1
|Download=Lecture-10.pdf,Lecture-10.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 11
|Room=APB E005
|Date=2019/01/14
|DS=DS1
|Download=Lecture-11.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture 12
|Room=APB E005
|Date=2019/01/18
|DS=DS1
|Download=Lecture-12.pdf,Lecture-12.pdf,
}}
{{Vorlesung Zeiten
|Lehrveranstaltungstype=Vorlesung
|Title=Lecture Ontology Learning (for your reference only - not part of exam)
|Room=APB E005
|Date=2019/01/25
|DS=DS1
|Download=Lecture-13.pdf,
}}
}}

Aktuelle Version vom 27. Januar 2019, 17:46 Uhr

Semantic Computing

Lehrveranstaltung mit SWS 2/2/0 (Vorlesung/Übung/Praktikum) in WS 2018

Dozent

Tutor

Umfang (SWS)

  • 2/2/0

Module

Leistungskontrolle

  • Mündliche Prüfung


Semantic computing tackles the computational understanding of meanings of contents and their machine readable representation. It refers to a set of methods for machines to acquire common-sense and linguistic knowledge. This introductory course covers a broad overview of fundamental theories and methodologies in semantic computing, such as an introduction to linguistics and Natural Language Processing (NLP), and a basic practical skill set of machine learning methods for natural language understanding, such as word embeddings, knowledge graph embeddings, Support Vector Machines (SVM), and neural networks. For neural networks the focus of this semester will be on the recurrent type (RNNs) and in terms of machine learning paradigms we will discuss selected algorithms of supervised, unsupervised, and reinforcement learning. At the very end, the course will look at how to employ the discussed methods to learn structured knowledge, such as ontologies, from natural language text.

Schedule

The lecture will take place each Monday as follows with the first lecture on 19 October 2018 and the last on 01 February 2019.

  • Friday, 9.20 -- 10.50 am (lecture), Room APB/E005
  • Friday, 11.10 -- 12.40 am (tutorial), Room APB/E005 or PC-Pool APB-E065
  • Monday, 14.1.2019, 11.10 --12.40 am (lecture), Room APB/E007
  • Monday, 14.1.2019, 1.00 -- 2.30 pm (tutorial, Room APB/E0069

Lecture free periods are:

  • Friday, 12 October 2018
  • Friday, 14 December 2018 (instead: Monday 14.1.2019 11.10 -- 2.30 pm; see above)
  • Saturday, 22 December 2018 to Sunday 06 January 2019

Materials

Lecture

All slides of lectures for implementations will be provided here shortly before each lecture (in "Dates and Materials" on this page).

Tutorial

All materials for the tutorials can be found at this linked Semantic Computing GitHub.

Platforms

This lecture uses the e-learning platform OPAL to support learning, discussing and exchanging contents related to the topic and lecture of Semantic Computing. Please go to THIS LINK, login with your ZIH login and join the Semantic Computing online platform. Feel free to add contents to the Wiki which is currently an infant and with your help will grow over the course of the semester. In other words, if you read or see contents potentially interesting to others, feel free to summarizes it in your words in SemComp Wiki.

Contact

Questions are encouraged during the lecture. If you wish to discuss any questions or issues offline, feel free to post into the Semantic Computing forum on the OPAL platform or e-mail me. There will be no official office hour. For personal appointments (office APB 2036), please also send me an e-mail.

Lecture slides and tutorials provide a broad overview on the wide range of topics covered in this lecture. For those interested in further readings to achieve a deeper understanding of individual topics the following literature listing might be useful. Please be aware that this list is not complete and I might update it based on topics coming up in class throughout the semester.

Resources

Machine Learning

  • Mitchell, T. M. (1997). Machine Learning, McGraw-Hill Higher Education. New York.
  • Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer.
  • Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press, Cambridge, MA, USA.
  • Grus, J. (2015). Data Science from Scratch. O'Reilly Media.
  • Chapelle, O., Schölkopf, B. and Zien, A. (2006). Semi-supervised learning. MIT Press. online edition
  • Witten, I. H., Frank, E., Hall, M. A. and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
  • Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning, online edition

Linguistics

  • Lappin, S. (2008). An Introduction to Formal Semantics. In: The Handbook of Linguistics, Wiley, pp. 369-393, online edition
  • Yule, G. (2014). The Study of Language. Cambridge. 5th ed. Cambridge University Press.
  • Cruse, A. (2011). Meaning in Language: An Introduction to Semantics and Pragmatics. Oxford University Press.
  • O'Grady, W., Archibald, J. and Katamba , F. (2009). Contemporary Linguistics: An Introduction. 6th ed. Bedford / St. Martin's, 2009.
  • Crystal, D. (1997). The Cambridge Encyclopedia of Language. Cambridge University Press.

Computational Linguistics

  • Jurafsky, D. and Martin, J.H. (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall, online 2nd edition, partial 3rd edition
  • Bird, S., Klein, E. and Loper, E. (2009). Natural Language Processing with Python. O'Reilly Media, online edition
  • Manning, C.D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, Massachusetts.
  • Manning, C.D., Raghavan, P. and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. online edition

Finding Conference Contributions

Veranstaltungskalender abonnieren (icalendar)

Vorlesung Lecture 1 DS2, 19. Oktober 2018 in APB E005 Datei
Vorlesung Lecture 2 DS1, 26. Oktober 2018 in APB E005 Datei 1,  Datei 2
Vorlesung Lecture 3 DS1, 2. November 2018 in APB E005 Datei
Vorlesung Lecture 4 DS1, 9. November 2018 in APB E005 Datei 1,  Datei 2,  Datei 3,  Datei 4
Vorlesung Lecture 5 DS1, 16. November 2018 in APB E005 Datei
Vorlesung Lecture 6 DS1, 23. November 2018 in APB E005 Datei 1,  Datei 2,  Datei 3
Vorlesung Lecture 7 DS1, 30. November 2018 in APB E005 Datei 1,  Datei 2
Vorlesung Lecture 8 DS1, 6. Dezember 2018 in APB E005 Datei
Vorlesung Lecture 9 DS1, 20. Dezember 2018 in APB E005 Datei
Vorlesung Lecture 10 DS1, 11. Januar 2019 in APB E005 Datei 1,  Datei 2
Vorlesung Lecture 11 DS1, 14. Januar 2019 in APB E005 Datei
Vorlesung Lecture 12 DS1, 18. Januar 2019 in APB E005 Datei 1,  Datei 2
Vorlesung Lecture Ontology Learning (for your reference only - not part of exam) DS1, 25. Januar 2019 in APB E005 Datei


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