Florent Nicart

NOAM: news outlets analysis and monitoring system

We present NOAM, an integrated platform for the monitoring and analysis of news media content. NOAM is the data management system behind various applications and scientific studies aiming at modelling the mediasphere. The system is also intended to address the need in the AI community for platforms where various AI technologies are integrated and deployed in the real world. It combines a relational database (DB) with state of the art AI technologies, including data mining, machine learning and natural language processing. These technologies are organised in a robust, distributed architecture of collaborating modules, that are used to populate and annotate the DB. NOAM manages tens of millions of news items in multiple languages, automatically annotating them in order to enable queries based on their semantic properties. The system also includes a unified user interface for interacting with its various modules.

Finding surprising patterns in textual data streams

We address the task of detecting surprising patterns in large textual data streams. These can reveal events in the real world when the data streams are generated by online news media, emails, Twitter feeds, movie subtitles, scientific publications, and more. The volume of interest in such text streams often exceeds human capacity for analysis, such that automatic pattern recognition tools are indispensable. In particular, we are interested in surprising changes in the frequency of n-grams of words, or more generally of symbols from an unlimited alphabet size. Despite the exponentially large number of possible n-grams in the size of the alphabet (which is itself unbounded), we show how these can be detected efficiently. To this end, we rely on a data structure known as a generalised suffix tree, which is additionally annotated with a limited amount of statistical information. Crucially, we show how the generalised suffix tree as well as these statistical annotations can efficiently be updated in an on-line fashion.

WFSC: a new weighted finite state compiler

This article presents a new tool, WFSC, for creating, manipulating, and applying weighted finite state automata. It inherits some powerful features from Xeroxs non-weighted XFST tool and represents a continuation of Xeroxs work in the field of finite state automata over two decades. The design is generic: algorithms work on abstract components of automata and on a generic abstract semiring, and are independent of their concrete realizations. Applications can access WFSCs functions through an API or create automata through an end-user interface, either from an enumeration of their states and transitions or from rational expressions.

Computing the follow automaton of an expression

Small nondeterministic recognizers are very useful in practical applications based on regular expression searching. The follow automaton, recently introduced by Ilie and Yu, is such a small recognizer, since it is a quotient of the position automaton. The aim of this paper is to present an efficient computation of this quotient, based on specific properties of the

FROM THE

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of the expression. The motivation is twofold. Since this structure is already a basic tool for computing the position automaton, Antimirov’s automaton and Hromkovic’s automaton, the design of an algorithm for computing the follow automaton via this structure makes it easier to compare all these small recognizers. Secondly such an algorithm provides a straightforward alternative to the rather sophisticated handling of e-transitions used in the original algorithm.

STRUCTURE OF A REGULAR EXPRESSION TO ITS FOLLOW AUTOMATON

The follow automaton of a regular expression, recently introduced by Ilie and Yu, is a quotient of the position automaton. The aim of this paper is to present an efficient computation of this quotient, based on specific properties of the structure of the expression.

A class of rational n -WFSM auto-intersections

Weighted finite-state machines with n tapes describe n-ary rational string relations. The join n-ary relation is very important in applications. It is shown how to compute it via a more simple operation, the auto-intersection. Join and auto-intersection generally do not preserve rationality. We define a class of triples (A,i,j) such that the auto-intersection of the machine A on tapes i and j can be computed by a delay-based algorithm. We point out how to extend this class and hope that it is sufficient for many practical applications.

WFSM Auto-intersection and Join Algorithms

The join of two n-ary string relations is a main operation regarding to applications. n-Ary rational string relations are realized by weighted finite-state machines with n tapes. We provide an algorithm that computes the join of two machines via a more simple operation, the auto-intersection. The two operations generally do not preserve rationality. A delay-based algorithm is described for the case of a single tape pair, as well as the class of auto-intersections that it handles. It is generalized to multiple tape pairs and some enhancements are discussed.

Constrained Expressions and their Derivatives

This paper proposes an extension to classical regular expressions by the addition of two operators allowing the inclusion of boolean formulae from the zeroth order logic. These expressions are called constrained expressions. The associated language is defined thanks to the notion of interpretation and of realization. We show that the language associated when both interpretation and realization are fixed is stricly regular and can be not regular otherwise. Furthermore, we use an extension of Antimirov partial derivatives in order to solve the membership test in the general case. Finally, we show that once the interpretation is fixed, the membership test of a word in the language denoted by a constrained expression can be undecidable whereas it is always decidable when the interpretation is not fixed.