• We adapt a traditional non-learnable GED algorithm to the novel paradigm of geometric deep learning. Triplet network for learning graph distances by means neural networks. Learning distance in domain without an embedding stage. Graph-based keyword spotting application with state-of-the-art performance. The emergence as framework deal graph-based representations has faded away approaches favor...

In this paper we present different solutions for the problem of indexing a dictionary of strings in compressed space. Given a pattern P , the index has to report all the strings in the dictionary having edit distance at most one with P . Our first solution is able to solve queries in (almost optimal) O(|P |+ occ) time where occ is the number of strings in the dictionary having edit distance at ...

In dendrochronology wood samples are dated according to the tree rings they contain. The dating process consists of comparing the sequence of tree ring widths in the sample to a dated master sequence. Assuming that a tree forms exactly one ring per year a simple sliding algorithm solves this matching task. But sometimes a tree produces no ring or even two rings in a year. If a sample sequence c...

This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead. The task is based on a new dataset of funny tweets posted in response to shared hashtags, collected from the ‘Hashtag Wars’ segment of the TV show @midnight. The results are evaluated in two subtasks that require...

Given a pattern string and a text, the task of approximate string matching is to find all locations in the text that are similar to the pattern. This type of search may be done for example in applications of spelling error correction or bioinformatics. Typically edit distance is used as the measure of similarity (or distance) between two strings. In this thesis we concentrate on unit-cost edit ...