Aaron L.-F. Han

NLP2CT meeting on 4th October, 2013

http://www.linkedin.com/in/aaronhan

Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory

Department of Computer and Information Science

University of Macau

23rd-27th, September, Darmstadt, Germany

5 days: workshops + tutorials + main conference

205 registrations

Around 30% percent foreigners

42% acceptance rate for long paper

Delegates from around 20 countries

Main conference: long paper + short paper + Nectar paper + Demos

5 workshops (4 before conference + 1 after conference)

5 tutorials before conference

3 invited talks during 3 days main conference

Invited talk one: Distributed Wikipedia LDA

By Massimiliano Ciaramita, research scientist at Google Zurich

http://research.google.com/pubs/MassimilianoCiaramita.html

Recent work on the disambiguation problem

Based on a novel distributed inference and representation framework that builds on Wikipedia, Latent Dirichlet Allocation and pipelines of MapReduce.

Invited talk two: Multimodal Sentiment Analysis

By Rada Mihalcea, Associate Professor in University of North Texas, US

http://www.cse.unt.edu/~rada/

A method that integrates linguistic, audio, and visual features for the purpose of identifying sentiment in online videos

Describe a novel dataset consisting of videos collected from the social media website YouTube and annotated for sentiment polarity at both video and utterance level

Joint use of visual, audio, and textual features greatly improves over the use of only one modality at a time

Run evaluations on datasets in English and Spanish

Invited talk three: Big Data and Text Analytics

By Hans Uszkoreit, Professor at Saarland University & Scientific Director at the German Research Center for Artificial Intelligence (DFKI)

http://www.coli.uni-saarland.de/~hansu/

Text analytics is faced with rapidly increasing volumes of language data

Big language data are not only a challenge for language technology but also an opportunity for obtaining application-specific language models that can cope with the long tail of linguistic creativity

Such models range from statistical models to large rule systems

Using examples from relation/event extraction, illustrate the exploitation of large-scale learning data for the acquisition of application specific syntactic and semantic knowledge

Five tutorials:

How to Design Effective Visualizations for Natural Language Processing

The Practitioner's Cookbook for Linked Lexical Resources

Natural Language Processing for Historical Texts

Text Analysis and Mining for Digital Humanities

Natural Language Processing and Rule-based Information Extraction with UIMA

Phrase Tagset Mapping for French and English Treebanks and Its Application in Machine Translation Evaluation

Aaron L.-F. Han, Derek F. Wong, Lidia S. Chao, Yervant Ho, Shuo Li, Lynn Zhu. GSCL 2013. LNCS Vol. 8105, pp. 119-131. Springer-Verlag Berlin Heidelberg.

Open source tool: https://github.com/aaronlifenghan/aaron-project-hppr

A Study of Chinese Word Segmentation Based on the Characteristics of Chinese

Aaron L.-F. Han, Derek F. Wong, Lidia S. Chao, Yervant Ho, Lynn Zhu, Shuo Li. GSCL 2013, LNCS Vol. 8105, pp. 111-118. Springer-Verlag Berlin Heidelberg.

Aaron L.-F. Han

NLP2CT meeting on 4th October, 2013

http://www.linkedin.com/in/aaronhan

Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory

Department of Computer and Information Science

University of Macau