Calais

Link: http://www.opencalais.com/

Intro: The OpenCalais Web Service automatically creates rich semantic metadata for the content you submit – in well under a second. Using natural language processing (NLP), machine learning and other methods, Calais analyzes your document and finds the entities within it. But, Calais goes well beyond classic entity identification and returns the facts and events hidden within your text as well.

Downloading full CiteSeerX data

Just saw this link and found it very interesting.

Link: http://b010.blogspot.com/2008/11/downloading-full-citeseerx-data.html

I copy here for backup (to avoid if the original link dies).

Steps for downloading the full dataset from CiteSeerX:

1. Download and extract the "Demo" from http://www.oclc.org/research/software/oai/harvester.htm
2. Go to the directory of the extracted files, type the following command to download the full dataset of CiteSeerX to the file "citeseerx_alldata.xml"
   java -classpath .;oaiharvester.jar;xerces.jar org.acme.oai.OAIReaderRawDump http://citeseerx.ist.psu.edu/oai2 -o citeseerx_alldata.xml

Thanks the author for that.

--
Cheers,
Vu

Preference Learning

Introduction: http://www.ke.tu-darmstadt.de/publications/papers/PLBook-Introduction.pdf

Applications to NLP???

Language Technology related Companies

Here, I will collect information about industry companies relating to developing and using Language Technology (LT). I would like to see how potential LT has in industry.

Worldwide

1) LingvoSoft

2) Bimaple
3) 

Vietnam

1) Vietgle (Lac Viet Company)
2) ViLangTek
3)

Parallel Text Mining for SMT

Problem: given a relatively large collection of parallel texts and a state-of-the-art SMT system, how to incrementally & automatically mine the parallel texts available on the Web. The newly added texts should ensure to improve the current SMT system.

Papers related:

1) Large Scale Parallel Document Mining for Machine Translation. COLING 2010. Link.
2) TBA

Crowdsourcing for NLP annotations

My new journal article: http://www.springerlink.com/content/n5q5n08853t06131/

Abstract

Crowd-sourcing has emerged as a new method for obtaining annotations for training models for machine learning. While many variants of this process exist, they largely differ in their methods of motivating subjects to contribute and the scale of their applications. To date, there has yet to be a study that helps the practitioner to decide what form an annotation application should take to best reach its objectives within the constraints of a project. To fill this gap, we provide a faceted analysis of crowdsourcing from a practitioner’s perspective, and show how our facets apply to existing published crowdsourced annotation applications. We then summarize how the major crowdsourcing genres fill different parts of this multi-dimensional space, which leads to our recommendations on the potential opportunities crowdsourcing offers to future annotation efforts. 

WebAnnotator

WebAnnotator is a new tool for annotating Web pages implemented at LIMSI. Giving it a try will take you no more than 10 minutes.

WebAnnotator is implemented as a Firefox extension, allowing annotation of both offline and inline pages. The HTML rendering is fully preserved and all annotations consist in new HTML spans with specific styles. 

WebAnnotator provides an easy and general-purpose framework and is made available under CeCILL free license (close to GNU GPL), so that use and further contributions are made simple.

WebAnnotator can be downloaded on the official Mozilla web page:
https://addons.mozilla.org/en-US/firefox/addon/webannotator/. 

A quick manual can be found here:
http://perso.limsi.fr/Individu/xtannier/en/WebAnnotator/

All parts of an HTML document can be annotated: text, images, videos, tables, menus, etc. The annotations are created by simply selecting a part of the document and clicking on the relevant type and subtypes. The annotated elements are then highlighted in a specific color. Annotation schemas can be defined by the user by creating a simple DTD representing the types and subtypes that must be highlighted. Finally, annotations can be saved (HTML with highlighted parts of documents) or exported (in a machine-readable format).

WebAnnotator will be presented at LREC conference in May 2012.