“Mining, Processing, and Reasoning over Textual Arguments”

Large amounts of text are added to the Web daily from social media, web-based commerce, scientific papers, eGovernment consultations, etc. Such texts are used to make decisions in the sense that people read the texts, carry out some informal analysis, and then (in the best case) make a decision; for example, a consumer might read the comments on an Amazon website about a camera before deciding what camera to buy. The problem is that such information is distributed, unstructured, and not cumulative. In addition, the argument structure – justifications for a claim and criticisms – might be implicit or explicit within some document, but harder to discern across documents. The sheer volume of information overwhelms users. Given all these problems, reasoning about arguments on the web is currently unfeasible.
A solution to these problems would be to develop tools to aggregate, synthesize, structure, summarize, and reason about arguments in texts. Such tools would enable users to search for particular topics and their justifications, trace through the argument (justifications for justifications and so on), as well as to systematically and formally reason about the graph of arguments. By doing so, a user would have a better, more systematic basis for making a decision. However, deep, manual analysis of texts is time-consuming, knowledge intensive, and thus unscalable. To acquire, generate, and transmit the arguments, we need scalable machine-based or machine-supported approaches to extract arguments. The application of tools to mine arguments would be very broad and deep given the variety of contexts where arguments appear and the purposes they are put to.

In this context, the goal of the Ph.D. is to address the following challenges:
I) define algorithms for automatically identifying arguments in texts. Th e goal is to detect, at an abstract level, the argumentative structures in texts . In addition, challenges like the automated discrimination between argumentative and non-argumentative text units, and the identification of reused, but manipulated, arguments which convey a different meaning than what was intended by their source, will be addressed .
ii) propose i ntra-argument mining algorithms, to automatically detect the internal structure of arguments. The goal is to analy ze and formaliz e the internal structure of the retrieved arguments, i.e., the identification of the relations that may hold between the arguments’ premises and conclusion, using the RTE (Recognizing Textual Entailment) framework.
i ii) propose inter-argument mining algorithms, to automatically detect relations between arguments. T he goal is to identif y the relations between arguments, extending RTE to account for more sophisticated relations like support, partial support, attack.

– Proceedings of the Workshop on Frontiers and Connections between Argumentation Theory and Natural Language Processing http://ceur-ws.org/Vol-1341/
– Lippi, M., Torroni, P., Argumentation Mining: State of the Art and Emerging Trends , ACM Transactions on Internet Technology, 2015.
– Elena Cabrio, Serena Villata :A natural language bipolar argumentation approach to support users in online debate interactions†. Argument & Computation 4(3) : 209-230 (2013)
– Available resources for Argument mining http://argumentationmining.disi.unibo.it/resources.html

Skills and profile:
• Master degree in Computer Science or Computational Linguistics is required.
• Programming skills.
• Basic knowledge of logic (propositional, first order) is preferred.
• Basic knowledge of Natural Language Processing and Machine Learning is preferred.
• Fluent English required, both oral and written. French is not mandatory.

Doctoral scholarship content:
The Université Côte d’Azur (UCA) Doctoral Program of Excellence offers scholarships consisting of an annual service-free contract with a salary of 2500 € per month, guaranteed for a maximum duration of four years.
This includes a three-year doctoral contract, plus an extra year that can be split between a pre-doctoral training and a post-doctoral project in order to value the thesis research results.
This comes with a support of up to 1500 € / year to defray the costs of participation in international conferences or workshops, together with funding for a six-month extra-doctoral activity consisting either of a collaborative visit to a foreign research institute or a collaborative study with a non-academic partner. Privileged conditions of accommodation will be offered.

Hosting team:
WIMMICS (http://wimmics.inria.fr/) is a research team of Université Côte d’Azur (UCA). The research fields of this team are graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities.

Location: I3S laboratory, Sophia Antipolis, France.

Applications : a curriculum vitae together with a motivation letter should be sent to Serena Villata: villata@i3s.unice.fr and Elena Cabrio: elena.cabrio@unice.fr .
Deadline for applications: March 1 st , 2017.


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