Research Engineer - Natural Language Argumentation on Twitter:
Retrieval of Argumentative Structures and Reasoning

We are looking for a Research Engineer with a background in Natural
Language Processing and Knowledge Representation and Reasoning to join
the Inria WIMMICS team (http://wimmics.inria.fr) and the start-up
Vigigloble (http://vigiglobe.com).
 
Understanding and interpreting the flow of messages exchanged in real
time on social platforms, like Twitter, raises several important
issues. The big amount of information exchanged on these platforms
represents a significant value for who is able to read and enrich this
multitude of information. Users directly provide this information, and
it is interesting to analyze such data both from the quantitative and
from the qualitative point of view, especially for what concerns
reputation and marketing issues (regarding brands, institutions or
public actors).

Moreover, the automated treatment of this type of data and the
constraints it presents (e.g., limited number of characters, tweets
with a particular writing style, amount of data, real-time
communication) offer a new and rich context for a challenging use of
existing tools for natural language processing. In particular, the
goal of the project is to address the following points:

- Automated selection and annotation of tweets, i.e., retrieval of
  those tweets that can be considered as arguments (like for instance
  those tweets whose content expresses an opinion on a given topic).

- Automated assignment of labels to the type of relation holding
  between arguments (positive relation or negative relation).

- Creation of an argumentation graph illustrating the relations
  between the arguments about a certain subject, and the further
  application of argumentation semantics to compute the set of
  "winning" arguments (i.e., the accepted arguments). This graph-based
  visualization provides a summary of the ongoing discussion on
  Twitter.
 
The first step of the project will be the creation and annotation of a
data set extracted from Twitter. This data set will be used as a
benchmark for training and testing NLP algorithms for the automated
detection of the relations between arguments. The second step will
consist in the adaptation of the framework presented in
[Cabrio&Villata, ECAI2012] to the Twitter scenario, with the aim to
make it more robust in terms of processing large volumes of data.
 
*Profile*
Mandatory requirements for applicants:

1. PhD in Computer Science;

2. Experience in NLP and Knowledge Representation and Reasoning, or in
a related field (Artificial Intelligence, Machine Learning...);

3. Hands-on experience of at least one programming language (e.g.,
Java, C++) ensure autonomy in completing the technical tasks of the
project;

4. Self-motivated, goal-oriented and willing to work in an
international team;

5. Pragmatic and customer oriented;

6. Fluent English is mandatory.
 
Optional:

1. Good control of scripting tools (bash, Unix/Linux tools) and of web
languages;

2. Experience with automation of NLP processing chains;

3. Fluent in French.
 
*Duration*
12 months
 
*Salary*
Gross Salary per month according to the level of diploma and the
experience in the domain: 2500 - 2800¤ / month (corresponding to
2100-2300¤ net salary / month)

*Deadline*
September 15th
 
*Contact*
Elena Cabrio: elena.cabrio@inria.fr
Valentino Marra: valentino@vigiglobe.com
Serena Villata: serena.villata@inria.fr
 
References
[Cabrio&Villata, ECAI2012]
Cabrio E., Villata S. Natural Language Arguments: A Combined
Approach. In Proceedings of the 20th European Conference on Artificial
Intelligence (ECAI 2012), p. 205-210, 2012.
 
NoDE Benchmark of Natural Language Arguments
http://www-sop.inria.fr/NoDE/

More information are available at 
http://www.inria.fr/institut/recrutement-metiers/offres/ingenieurs-r-d 
and at  
https://wimmics.inria.fr/node/50