#acl Niklas Schnelle:read,write Thomas Goette:read,write All:read = Welcome to the Wiki of the seminar ''Deep Natural Language Processing'' in the winter semester 2017/2018 = This seminar is organized by [[http://ad.informatik.uni-freiburg.de/staff/bast|Prof. Dr. Hannah Bast]] with the assistance of [[http://ad.informatik.uni-freiburg.de/staff/schnelle|Niklas Schnelle]] and [[mailto:thomas.goette@mars.uni-freiburg.de|Thomas Goette]]. The seminar will take place every Wednesday, 4:15 pm - 5:45 pm, in the seminar room 02-017 in building 52. There will be '''no''' session on Wednesday, October 25th, 2017, on Wednesday, November 1st, 2017 (All Hallows), on Wednesday, December 27th, 2017, and on Wednesday, January 3rd, 2018 (christmas break). '''See below for the recording and slides of the introductory session.''' === Important: Information === Not everyone is in the Daphne DeepNLP course yet, '''please join if you're taking this seminar! ''' == Important Links == Please register for the course in [[https://campus.uni-freiburg.de:443/qisserver/pages/startFlow.xhtml?_flowId=showEvent-flow&unitId=3513&termYear=2017&termTypeValueId=2&navigationPosition=studiesOffered,searchCourses|HISinOne]] '''and''' [[https://daphne.informatik.uni-freiburg.de/login/?service=/ws1718/DeepNlp/|Daphne]]. If you have questions (both general or specific to your topic), please ask a question on the [[https://daphne.informatik.uni-freiburg.de/forum/viewforum.php?f=774/|forum]] after registering for [[https://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/|Daphne]]. There is an introduction to SVN in [[http://ad-wiki.informatik.uni-freiburg.de/teaching/SVNEnglish|english]] and [[http://ad-wiki.informatik.uni-freiburg.de/teaching/SVN|german]]. == Modalities == Participants of the seminar will have to present one of the topics either alone or as a group of two. Each '''presentation''' will be 30 minutes for one participant or 2 * 20 minutes for two. In addition to introducing the topic each presentation must include a '''demo''' part where participants present a practical application of their topic. What exactly this demo entails depends on the topic and will be discussed with each person/team separately. While we will provide suggestions you are very welcome to bring in your own ideas. Examples for demos may include the implementation of a ''small application'', an ''interactive visualization'' or the ''demonstration of a complex existing system which you have set up on your own''. == Sessions == ||Session ||Date ||Topic || ||1 ||Wednesday, October 18th, 2017 ||'''Introduction and Organization''', [[http://www.youtube.com/watch?v=eeuwVMOH0Uk|Video Recording]] ([[http://ad-teaching.informatik.uni-freiburg.de/SeminarDeepNlpWS1718/session-01.mp4|Download]]), [[https://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-01.pdf|Slides]] || || ||Wednesday, October 25th, 2017 ||'''NO SESSION''' || || ||Wednesday, November 1st, 2017 ||'''NO SESSION''' || ||2 ||Wednesday, November 8th, 2017 ||'''Machine Learning Introduction''' (Niklas Schnelle) + topic assignment, [[https://www.youtube.com/watch?v=YWuUQK3NUqQ|Video Recording]] ([[http://ad-teaching.informatik.uni-freiburg.de/SeminarDeepNlpWS1718/session-02.mp4|Download]]), [[https://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-02-code/|Code]], [[https://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-02.pdf|Slides]] || ||3 ||Wednesday, November 15th, 2017 ||'''Deep Learning Introduction''' (Thomas Goette), [[https://www.youtube.com/watch?v=qAnsywbo3UU|Video Recording]] ([[http://ad-teaching.informatik.uni-freiburg.de/SeminarDeepNlpWS1718/session-03.mp4|Download]]), [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-03.pdf|Slides]] || ||4 ||Wednesday, November 22nd, 2017 ||'''Standard Language Model''' (Samuel Steinegger) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-04.pdf|Slides]] || ||5 ||Wednesday, November 29th, 2017 ||'''RNN Language Model''' (Ikrima Bin Saeed) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-05.pdf|Slides]] || ||6 ||Wednesday, December 6th, 2017 ||'''word2vec''' (Sameed Hayat, Salih Hasan Siddiqi) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-06.pdf|Slides]] || ||7 ||Wednesday, December 13rd, 2017 ||'''Paraphrasing and Synonyms''' (Christopher Krolla, Tobias Matysiak) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-07.pdf|Slides]] || ||8 ||Wednesday, December 20th, 2017 ||'''Convolutional Neural Networks for NLP''' (David-Elias Künstle, Jessica Pape) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-08.pdf|Slides]] || || ||Wednesday, December 27th, 2017 ||'''NO SESSION''' || || ||Wednesday, January 3rd, 2018 ||'''NO SESSION''' || ||9 ||Wednesday, January 10th, 2018 ||'''Text Classification''' (Frank Schüssele, Julian Schwarz) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-09.pdf|Slides]] || ||10 ||Wednesday, January 17th, 2018 ||'''Sentiment Analysis''' (Jasmin Denk, Joao Carvalho) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-10.pdf|Slides]] || ||11 ||--(Wednesday, January 24th, 2018)-- ('''moved to February 14th, 2018''') ||'''Attention''' (Omar Shehata) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-11.pdf|Slides]] || ||12 ||Wednesday, January 31st, 2018 ||'''Question Answering on Text''' (Dennis Koch, Philip Klein) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-12.pdf|Slides]] || ||13 ||Wednesday, February 7th, 2018 ||'''Question Answering on Knowledge Bases''' (Dennis Armbruster, Patrick Bumüller) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-13.pdf|Slides]] || ||'''Speech to Text''' (Mohammad Fattouh) [[http://daphne.informatik.uni-freiburg.de/ws1718/DeepNlp/svn/public/session-14.pdf|Slides]] || == Topics == The topics are going to be introduced and roughly explained in the first session. They are basically about how Deep Learning can be used in Natural Language Processing. {{{#!html Please note that you are supposed to present the topic, not the material listed here. The material is only intended as a starting point for your research. }}} * '''Standard Language Model''' * Modelling natural languages using unigrams, n-grams and statistics * Material: * [[https://web.stanford.edu/class/cs124/lec/languagemodeling.pdf|Lecture Notes from Stanford]] * '''RNN Language Model''' * Character based language modelling with Deep Neural Networks * Material: * [[http://karpathy.github.io/2015/05/21/rnn-effectiveness/|Andrej Karpathy - The Unreasonable Effectiveness of Recurrent Neural Networks]] * '''word2vec''' * Representing words as vectors from a high dimensional vector space * Material: * [[https://arxiv.org/abs/1301.3781|Original Paper by Mikolov et al]] * [[https://www.youtube.com/watch?v=wTp3P2UnTfQ|YouTube Video Explanation]] * [[https://radimrehurek.com/gensim/|Gensim framework for word vector generation]] * '''Paraphrasing and Synonyms''' * Challenges and approaches for handling the ambiguity of natural language * Material: * [[http://www.lrec-conf.org/proceedings/lrec2012/pdf/266_Paper.pdf|CrossWikis Wikipedia extracted synonyms]] * [[http://aclweb.org/anthology/P/P13/P13-1158.pdf|Question Paraphrasing]] (advanced) * '''Convolutional Neural Networks for NLP''' * A neural network architecture from image processing applied to NLP * Material: * [[http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/|Introductory Blog Post]] * [[https://github.com/facebookresearch/fairseq|CNN based Machine Translation by Facebook]] (advanced) * '''Text Classification''' * Classifying text using Machine Learning: Approaches and Techniques * Material: * [[https://medium.com/towards-data-science/machine-learning-nlp-text-classification-using-scikit-learn-python-and-nltk-c52b92a7c73a|Tutorial]] * '''Sentiment Analysis''' * Using NLP for emotion extraction: Approaches and Techniques * Material: * [[https://blog.openai.com/unsupervised-sentiment-neuron/|Accidentally discovered state of the art sentiment analysis by OpenAI]] * '''Attention''' * Attending to detail for Neural Networks * Material: * [[https://talbaumel.github.io/attention/|Learning string.reverse() with Attention]] * [[http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/|Introductory Blog Post]] * '''Question Answering on Text''' * Finding answers in a heap of text * Material: * [[https://rajpurkar.github.io/SQuAD-explorer/|SQuad Benchmark]] * '''Question Answering on Knowledge Bases''' * Finding answers in a warehouse of facts * Material: * [[https://www.microsoft.com/en-us/research/publication/semantic-parsing-via-staged-query-graph-generation-question-answering-with-knowledge-base/|Microsoft Research QA system STAGG]] * [[http://ad-publications.informatik.uni-freiburg.de/CIKM_freebase_qa_BH_2015.pdf|Our QA system Aqqu (code on request)]] == General Additional Information == The following links are universally useful for all topics * Deep Learning for NLP at Stanford: [[https://nlp.stanford.edu/courses/NAACL2013/|older]] and [[http://web.stanford.edu/class/cs224n/syllabus.html|newer]] * Deep Learning [[http://www.deeplearningbook.org/|the book]] by Ian Goodfellow * Yoav Goldberg’s [[http://u.cs.biu.ac.il/~yogo/nnlp.pdf|Primer on NNs for NLP]] Mainly for Question Answering: * Our chair's [[http://ad-publications.informatik.uni-freiburg.de/FNTIR_semanticsearch_BBH_2016.pdf|Survey on Semantic Search]]