The 1st Workshop on Teaching NLP for Digital Humanities (Teach4DH)

—  Deadline extension: Sunday July, 23  —

https://teach4dh.github.io

Held in conjunction with GSCL 2017 (http://gscl2017.dfki.de/),
Berlin, Germany, September 12, 2017

WORKSHOP DESCRIPTION
=======================

Computational linguists often teach digital humanities (DH) modules that focus on using / applying / adapting NLP technologies and resources to DH problems. The challenge in such modules is to introduce students with a background in humanities or social sciences to technical content without going into detail. NLP tools often require special input formats, knowledge of program options, and post-processing of the output. NLP resources are often not easily accessible and provide only limited interaction. Interfaces and search tools require knowledge of specialized search syntax.

The workshop is intended to provide a forum for such ?NLP teachers? to share experiences, discuss best practices, introduce teaching concepts, and present demos of existing technology. It also provides an opportunity for DH researchers to express their needs and provide directions for future DH curricular developments. The workshop is intended to foster collaborations and to cross-fertilize knowledge and approaches across DH disciplines.

Teach4DH is co-organized by GSCL’s SIG Education and Profession and supported by CLARIN.

WORKSHOP TOPICS
=======================

We welcome submissions of long and short papers, posters, and demonstrations relating to any aspect of teaching NLP in DH classes, including:

— Didactic units / concepts / tools for employing NLP in teaching «on stage»
— Challenges in teaching for a non-CL audience
— Exploiting NLP tools «behind the scenes» for creating didactic DH material
— Curricular considerations in developing standards for DH programs

See the website at https://teach4dh.github.io for more details, including submission instructions. If you have any questions, please feel free to contact the workshop co-chairs at teach4dh@googlegroups.com<mailto:teach4dh@googlegroups.com>.

IMPORTANT DATES
=====================

All submission deadlines are at 11:59 p.m. PST

*** Jul 23rd, 2017 Extended deadline ***
Aug 09th, 2017: Notification of acceptance
Aug 25th, 2017: Camera-ready papers due
Sep 12th, 2017: Workshop day

COMMITTEES
=====================

WORKSHOP CHAIRS

Peggy Bockwinkel University of Stuttgart
Thierry Declerck DFKI, Saarbrücken
Sandra Kübler Indiana University, Bloomington
Heike Zinsmeister Universität Hamburg

PROGRAM COMMITTEE

Melanie Andresen Universität Hamburg
Fabian Barteld Universität Hamburg
Sabine Bartsch Technische Universität Darmstadt
Noah Bubenhofer Zurich University
Stefanie Dipper Ruhr-Universität Bochum
Kim Gerdes Sorbonne nouvelle Paris
Evelyn Gius Universität Hamburg
Fotis Jannidis Julius-Maximilians-Universität Würzburg
Jonas Kuhn University of Stuttgart
Lothar Lemnitzer BBAW, Berlin
Harald Lüngen IDS, Mannheim
Nils Reiter University of Stuttgart
Thomas Schmidt IDS, Mannheim
Ulrike Schneider Johannes-Gutenberg-Universität Mainz
Julian Schulz LMU, München
Olga Scrivner Indiana University
Caroline Sporleder Georg-August-Universität Göttingen
Jannik Strötgen MPI, Saarbrücken
Thorsten Trippel University of Tübingen
Gabriel Viehhauser University of Stuttgart
Andreas Witt University of Cologne
Amir Zeldes Georgetown University

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PhD position in deep learning and NLP

Overview

A PhD position is available within an industrially supported project. The
project seeks to develop deep learning methods for predictive analysis of
complex network data (e.g., social networks, trading networks etc),
focusing on statistical modelling of information exchange in transaction
networks. This is an on-going project with 2 PIs: Ivan Titov
and Max Welling
<https://staff.fnwi.uva.nl/m.welling/>.

The student will be affiliated with the University of Amsterdam, but will
be mostly located at the University of Edinburgh, hosted by the Institute
of Language, Cognition and Computation (ILCC) <http://web.inf.ed.ac.uk/ilcc>
and Edinburgh NLP group <http://groups.inf.ed.ac.uk/edinburghnlp/people>.
ILCC is the largest lab focusing on NLP in Europe and one of the largest in
the world, with over 10 faculty doing research in this area. This PhD
vacancy will focus primarily on deep learning for natural language
processing (supervised by Ivan Titov)

We are exploiting data provided by an industrial partner. As information in
these networks mostly comes in a textual form, we are developing methods
for inducing predictive semantics representations of texts, relying both on
the text itself but also on the flow of information in the network.

Company:

University of Edinburgh

Educational level:

Master Degree

How to apply:

http://www.uva.nl/en/content/vacancies/2017/07/17-363-phd-candidate-in-deep-learning-for-natural-language-processing.html?n

About The University of Edinburgh

The University’s mission is the creation, dissemination and curation of knowledge.

As a world-leading centre of academic excellence we aim to:

Enhance our position as one of the world’s leading research and teaching universities and to measure our performance against the highest international standards.

Provide the highest quality learning and teaching environment for the greater wellbeing of our students.

Produce graduates fully equipped to achieve the highest personal and professional standards.

Make a significant, sustainable and socially responsible contribution to Scotland, the UK and the world, promoting health and economic and cultural wellbeing.

As a great civic university, Edinburgh especially values its intellectual and economic relationship with the Scottish community that forms its base and provides the foundation from which it will continue to look to the widest international horizons, enriching both itself and Scotland.

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CFP: ECIR 2018

В этом году я не успела ничего подать, а мои позапрошлогодние впечатление от ECIR можно найти вот тут. Надо будет в этом году постараться поучаствовать.

ECIR 2018 — European Conference on Information RetrievalCall for
long/short papers & demo

Grenoble, France — March 26-29, 2018

http://www.ecir2018.org/

*****************************

*

Full Papers, Short Papers and Demonstrations

We are seeking the submission of high-quality and original full papers,
short papers and demos. Submissions will be reviewed by experts on the
basis of the originality of the work, the validity of the results,
chosen methodology, writing quality and the overall contribution to the
field of IR. Short Paper submissions addressing any of the areas
identified in the conference topics are also invited. Authors are
encouraged to describe work in progress and late-breaking research
results. Demonstrations present research prototypes or operational
systems. They provide opportunities to exchange ideas gained from
implementing IR systems and to obtain feedback from expert users.
Demonstration submissions are welcome in any of the conference topic areas.

Reproducible IR Research Track

We are happy to announce that the Reproducible IR Research Track
introduced at ECIR 2015 will continue for ECIR 2018. Reproducibility is
critical for establishing reliable, referenceable and extensible
research for the future. Experimental papers are therefore most useful
when their results can be tested and generalised by peers. This track
specifically invites submission of papers reproducing a single or a
group of papers, from a third-party where you have *NOT* been directly
involved (e.g., *not* been an author or a collaborator). Emphasise your
motivation for selecting the paper/papers, the process of how results
have been attempted to be reproduced (successful or not), the
communication that was necessary to gather all information, the
potential difficulties encountered and the result of the process. A
successful reproduction of the work is not a requirement, but it is
important to provide a clear and rigid evaluation of the process to
allow lessons to be learned for the future.

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магистерская программа Теоретическая лингвистика и описание языка (ВШЭ)

Магистерская программа Теоретическая лингвистика и описание языка продолжает набор магистрантов. Обучение ведется на английском языке, форма обучения: очная, вечерняя. Количество бюджетных мест — 20.
Отличительными чертами программы является включение междисциплинарного компонента (курсы экспериментальной и антропологической лингвистики, социолингвистики и инструментальной фонетики), акцент на анализе языковых данных (курс методов и практики статистического анализа), миникурсы по грамматике разноструктурных языков, а также возможность активно сочетать учебные курсы с самостоятельной исследовательской деятельностью. В рамках программы проводятся лекции и курсы приглашенных специалистов; осенью 2017 г. запланированы курсы Джоханны Николс (Johanna Nichols, University of California, Berkeley) и Стефана Гриса (Stefan Gries, University of California, Santa Barbara). Институциональными научными партнерами программы являются, в рамках НИУ ВШЭ, Лаборатория нейролингвистики, Лаборатория языковой конвергенции и Лаборатория языков Кавказа. У программы установлены образовательные и научно-исследовательские контакты с Полярным университетом Тромсе и другими европейскими университетами.
Школа лингвистики НИУ ВШЭ, на базе которой существует программа — центр современных лингвистических исследований самой широкой тематики, от корпусных исследований исторической грамматики и лексики русского языка до акустического анализа консонантных систем языков Дагестана. Магистры имеют возможность активно участвовать в исследовательских проектах Школы и ее партнеров, как теоретического, так и более прикладного характера. Один из ближайших учебных проектов Школы лингвистики — Летняя школа по лексике и типологии.
Подробнее о магистерской программе можно прочитать здесь. Обратите внимание — в рамках приемной кампании 2017 г. срок подачи документов заканчивается 20 июля.
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Linked Data for Information Extraction LD4IE @ISWC2017

LD4IE 2017
The 5th international Workshop on Linked Data for Information Extraction
in conjunction with the 16th International Semantic Web Conference (ISWC 2017)
Vienna, Austria, October 21-25, 2017
http://iswc2017.semanticweb.org/

Workshop website: http://w3id.org/ld4ie
Twitter: @LD4IE #LD4IE #LD4IE2017
Facebook page: Ld4ie2017 (at https://www.facebook.com/Ld4ie)

*************** Important Dates ***************

Abstract submission deadline: July 14, 2017
Paper submission deadline: July 21, 2017
Acceptance Notification: August 24, 2017
Camera-ready versions: September 1, 2017
Workshop date: to be announced (October 21-22, 2017)

*************** Call for Papers ***************

This workshop focuses on the exploitation of Linked Data for Web Scale Information Extraction (IE), which concerns extracting structured knowledge from unstructured/semi-structured documents on the Web.
One of the major bottlenecks for the current state of the art in IE is the availability of learning materials (e.g., seed data, training corpora), which, typically are manually created and are expensive to build and maintain.
Linked Data (LD) defines best practices for exposing, sharing, and connecting data, information, and knowledge on the Semantic Web using uniform means such as URIs and RDF.
It has so far been created a gigantic knowledge source of Linked Open Data (LOD), which constitutes a mine of learning materials for IE.
However, the massive quantity requires efficient learning algorithms and the unguaranteed quality of data requires robust methods to handle redundancy and noise.
LD4IE intends to gather researchers and practitioners to address multiple challenges arising from the usage of LD as learning material for IE tasks, focusing on (i) modelling user defined extraction tasks using LD; (ii) gathering learning materials from LD assuring quality (training data selection, cleaning, feature selection etc.); (ii) robust learning algorithms for handling LD; (iv) publishing IE results to the LOD cloud.

*************** Research Topics ***************

Topics of interest include, but are not limited to:

Topics
* Modelling Extraction Tasks
** extracting knowledge patterns for task modelling
** user friendly approaches for querying linked data

* Information Extraction
** selecting relevant portions of LOD as training data
** selecting relevant knowledge resources from linked data
** IE methods robust to noise in training data
** Information Extractions tasks/applications exploiting LOD (Wrapper induction, Table interpretation, IE from unstructured data, Named Entity Recognition, …)
** Domain specific IE consuming and producing LOD (social data, scholarly data, health data, …)
** publishing information extraction results as Linked Data
** linking extracted information to existing LOD datasets

* Linked Data for Learning
** assessing the quality of LOD data for training
** select optimal subset of LOD to seed learning
** managing incompleteness, noise, and uncertainty of LOD
** scalable learning methods
** pattern extraction from LOD

*************** Submission ********************

All submissions must be written in English.
We accept the following formats of submissions:
Full paper with a maximum of 12 pages including references.
Short paper with a maximum of 6 pages including references.

Two formats are possible for the submission: PDF and HTML.

PDF submissions must be formatted according to the information for LNCS Authors (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.).

We would like to encourage you to submit your paper as HTML, in which case you need to submit a zip archive containing an HTML file and all used resources.
If you are new to HTML submission these are good places to start:
* dokieli (https://github.com/linkeddata/dokieli) is a clientside editor for decentralised article publishing, annotations and social interactions. It is compliant with the Linked Research (https://linkedresearch.org/) initiative. Example papers using LNCS and ACM: http://csarven.ca/dokieli and on website https://dokie.li/.
* Research Articles in Simplified HTML (RASH) format: documentation and stylesheets at https://github.com/essepuntato/rash

In order to check if your HTML submission is compliant with the page limit constraint, please use one of the LNCS layouts and printing/storing it as PDF.

Please submit your contributions electronically in PDF or HTML format to EasyChair at https://www.easychair.org/conferences/?conf=ld4ie2017
When submitting your paper, select the appropriate topic between:
* Research — long paper
* Research — short paper

Accepted papers will be published online via CEUR-WS.

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Johns Hopkins University: Postdoc Positions

The Center for Language and Speech Processing (CLSP) at the Johns
Hopkins University seeks applicants for postdoctoral fellowship
positions in speech and language processing, including the areas of
natural language processing, machine learning and health
informatics. Applicants must have a Ph.D. in a relevant discipline and
a strong research record.

Possible research topics include:
— Trend Detection in Social Media
— Analysis of Clinical Medical Text
— Topic Identification and Analysis from Text
— Broadly Multilingual Learning of Morphology and Low-Resource Machine
Translation
— NLP and Machine Learning for Clinical Data Analysis

Host faculty include:
Mark Dredze, David Yarowsky, Jason Eisner, Sanjeev Khudanpur, Benjamin
Van Durme

Details and application information:
http://www.clsp.jhu.edu/employment-opportunities/

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Postdoctoral Fellow in Machine Learning and Computational Linguistics

We are seeking a skilled postdoctoral fellow whose expertise intersects
machine learning and computational linguistics. The candidate is expected
to make novel contributions to these disciplines in the context of
healthcare. The domain of the research is largely open-ended. This may
include textual processing of the medical record, speech recognition with
atypical or pathological voices, and human-computer dialogue using modern
recurrent neural networks, especially with situated robots.

Work can commence as soon as August 2017. The initial contract is for 1
year although extension is possible.

The successful applicant will have:
1) A doctoral degree in a relevant field of computer science,
electrical engineering, biomedical engineering, or a relevant
discipline;
2) Evidence of impact in research through a strong publication record
in relevant venues;
3) Evidence of strong collaborative skills, including possible
supervision of junior researchers, students, or equivalent industrial
experience;
4) Excellent interpersonal, written, and oral communication skills;
5) A strong technical background in machine learning, natural
language processing, and speech recognition. Experience in
human-computer interaction is an asset. Experience with clinical
populations is preferred.

This work will be conducted at the Toronto Rehabilitation Institute and at
the University of Toronto.

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THREE (3) POSTDOC­TORAL RE­SEARCH­ERS (DI­GITAL HU­MAN­IT­IES)

https://www.helsinki.fi/en/open-positions/three-3-postdoctoral-researchers-digital-humanities

The University of Helsinki – among the best in the world!

Founded in 1640, the University of Helsinki is one of the best multidisciplinary research universities in the world. The high-quality research carried out by the university creates new knowledge for educating diverse specialists in various fields, and for utilisation in social decision-making and the business sector.

The Faculty of Arts is a significant international community fostering research, education and cultural interaction. The Faculty has 7,000 undergraduate and postgraduate students and employs 500 experts.

Heldig is a newly-founded Digital Humanities Centre at the University of Helsinki. The objective of Heldig is to foster the combining of computational methods in humanities and social science and study different phenomena related to digitalization. Heldig also provides teaching in digital humanities. One of the research strands in Heldig is the historical and linguistic study of public discourse, conceptual change and knowledge production.

The Faculty of Arts invites applications for

THREE (3) POSTDOCTORAL RESEARCHERS (DIGITAL HUMANITIES)

for a fixed term period from 1 September 2017 onwards (or as agreed) for a maximum of three years. The period (1-3 years) depends of the candidate’s research plan.

We are looking for candidates with expertise on computational science, linguistics and history/cultural heritage. The three successful candidates will be members of a research community that already includes, for example, Academy of Finland’s project on “Computational History and the Transformation of Public Discourse”, 2015-2019. The data that the research community is using includes various historical full-text collections and large metadata collections, mainly in English, Finnish and Swedish. The group is particularly interested in studying conceptual change, intertextuality based on text-reuse and statistical analysis of knowledge production.

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PAN shared task on Gender Identification in Russian texts

PAN shared task on Gender Identification in Russian texts (RusProfiling) http://en.rusprofilinglab.ru/rusprofiling-at-pan/

held in conjunction with the FIRE 2017 Forum for Information Retrieval Evaluation

8th — 10th December 2017, Bangalore

Author profiling consists of predicting an author’s demographics from his/her writing, with gender identification being the most popular task. Slavic languages, however, are less investigated from author profiling standpoint and have never been presented at PAN.

This year we introduce a PAN shared task on Cross-genre Gender Identification in Russian texts where we will provide as training dataset tweets and as test dataset tweets, Facebook posts, as well as reviews, texts describing images, or letters to a friend.

We cordially invite all researchers and practitioners from all fields to participate in this year’s PAN @ FIRE shared task.

———————————————-
Important Dates
———————————————-

  • 30th June, 2017 Release of training corpus (training period starts)
  • 1st September, 2017 Release of test corpus
  • 20th September, 2017 Submission of runs
  • 27th September, 2017 Results notification
  • 15th October, 2017 Working notes due

 

Task Coordinators

Tatiana Litvinova, RusProfiling Lab, Russia

Pavel Seredin, RusProfiling Lab, Russia

Olga Litvinova, RusProfiling Lab, Russia

Paolo Rosso, PRHLT research centre, Universitat Politècnica de València, Spain

Francisco Rangel, PRHLT research centre, Universitat Politècnica de València and Autoritas Consulting, Spain

———————————————-
Contact
———————————————-

E-mail: centrrusya[at]gmail.com
Track Web page: http://en.rusprofilinglab.ru/rusprofiling-at-pan/

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RANLP 2017 STUDENT WORKSHOP

RANLP 2017 STUDENT WORKSHOP
Hotel “Cherno More”, Varna, Bulgaria
4 — 6, September 2017 (during RANLP 2017)
http://lml.bas.bg/ranlp2017/ranlpStud2017/ [1]

Further to the previous successful and highly competitive Student Research
Workshops associated with the conference &#039;Recent Advances in Natural Language
Processing&#039; (RANLP, in 2009, 2011, 2013, and 2015), we are pleased to
announce the fifth edition of the workshop which will be held during the main
RANLP 2017 conference days on 4-6 September 2017. For the first time the
conference and the workshop will take place at the Black Sea city of Varna,
Bulgaria.

The International Conference RANLP 2017 would like to invite students at all
levels (Bachelor-, Master-, and PhD-students) to present their ongoing or
completed work at the Student Research Workshop. We invite two types of
student submissions:
— Full Papers – unpublished original research of the student.
— Short Papers – either a work in progress or a research proposal.

The aim of this workshop is to facilitate the exchange of knowledge between
young researchers by providing an excellent opportunity to present and
discuss their work in progress or completed projects to an international
research audience and receive feedback from senior researchers. The research
being presented can come from any topic area within Natural Language
Processing (NLP) and computational linguistics, including but not limited to
the following topic areas:

phonetics and phonology, morphology, lexicon, syntax, semantics, discourse,
pragmatics, dialogue, mathematical foundations, formal grammars and
languages, finite-state technology, statistical models for natural language
processing, machine learning, word embeddings, deep learning for NLP,
similarity, evaluation, sublanguages and controlled languages, lexicography,
language resources and corpora, terminology, corpus annotation, ontologies,
complexity, text segmentation, POS tagging, parsing, semantic role labelling,
word-sense disambiguation, computational treatment of multiword expressions,
textual entailment, anaphora and coreference resolution, temporal processing,
natural language generation, speech recognition, text-to-speech synthesis,
knowledge acquisition, text categorisation, machine translation, including
statistical machine translation and neural machine translation, translation
technology including translation memory systems, information retrieval,
information extraction, event extraction, question answering, text
summarisation, term extraction, text and web mining, opinion mining and
sentiment analysis, multimodal systems, natural language processing for
educational applications, automated writing assistance, text simplification,
NLP for biomedical texts, author profiling and related applications,
application-orientated papers related to NLP, chatbots, fact checking,
computer-aided language learning, stance detection, computational cognitive
modelling, dialect processing, language and vision, multilingual NLP, NLP for
language disorders, NLP for social media, NLP for the semantic web, patents
search, theoretical NLP, theoretical papers related to NLP.

Papers at the borderline between two sciences (such as Translation Studies,
Psycholinguistics, etc.), but bearing contributions to NLP will be also
accepted for review. All accepted papers will be presented at the Student
Workshop sessions during the main conference days: 4-6 September 2017. The
articles will be issued in a special Student Session proceedings and uploaded
to the ACL Anthology.

IMPORTANT DATES

Submission deadline: 04 July 2017
Acceptance notification: 10 August 2017
Camera-ready deadline: 20 August 2017
Workshop: 04-06 September 2017
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