29.1876, Support: Computational Linguistics; Discourse Analysis: PhD, Université de Lorraine

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LINGUIST List: Vol-29-1876. Thu May 03 2018. ISSN: 1069 - 4875.

Subject: 29.1876, Support:  Computational Linguistics; Discourse Analysis: PhD, Université de Lorraine

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Date: Thu, 03 May 2018 11:48:38
From: Mathilde Dargnat [mathilde.dargnat at atilf.fr]
Subject: Computational Linguistics; Discourse Analysis: PhD, Université de Lorraine, France

 Institution/Organization: Université de Lorraine - France 
Department: Computational Linguistics 

Level: PhD 

Duties: Research
 
Specialty Areas: Computational Linguistics; Discourse Analysis 
 

Description:

Text mining is widely used in many different domains in order to classify
opinions, to analyse sentiments, to acquire and represent knowledge or to
understand complex processes. One common feature to these approaches is that
they should be appliable to a large amount of real raw texts. At present, we
may distinguish two main types of approaches. One concerns mainly
classification of texts into categories, typically identifying if a text
correspond to a positive or a negative opinion. The second type concerns the
extraction of knowledge from texts. It usually requires several steps like
information extraction (identification of domain entities, relations between
them) and then a conceptualisation step to organise information into knowledge
units (data mining tools). 

There is one very challenging dimension that has always been neglicted in text
mining: the disourse level. And we claim that this is the next step to
properly understand the content of documents. So what means “discourse level”
and what could it be used for? There exist several discourse theories in
computational linguistics but for sake of simplicity, we will consider here
that the discourse level relates some parts of a text (discourse units) with
some others, of the same text, making explicit the kind of relation between
them: one sentence may elaborate on the previous one, another sentence gives
the cause of a previous event. . . In other words, discourse structures make
texts different from a simple juxtaposition of sentences.

Discourse relations can thus be used to better understand causes,
consequences, temporal order between events. . . Today, many companies crawl
the web to collect reviews of products or services. While sentiment analysis
or opinion mining currently assign a positive or a negative flag and provide
some keywords to explain the result, discourse may explain what are the main
arguments, what is the sequence of events or what are the main reasons that
make the customer positive or negative. In a scientific domain (ex. medical
domain), discourse structure enables a better understanding of the temporal
order of symptoms or the onset of diseases, the effects and side effects of a
treatment...

Recent research advances in linguistics, in natural language processing, in
classification, graph mining and in (deep) learning, all contribute to define
a new paradigm to propose new methods for mining texts at the discourse level.
The thesis should thus explore different formalisms, specify the goal of
discourse mining and combine methods coming from several domains. Indeed,
discourse representations are complex structures that can be compared (to
extract similar parts of texts) or classified.
 

Application Deadline: 15-May-2018 

Web Address for Applications: https://bit.ly/2FFhL0d 

Contact Information: 
	Pr Yannick Toussaint 
	yannick.toussaint at loria.fr



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