Frédéric Landragin

When viewing natural scenes, do abnormal colors impact on spatial or temporal parameters of eye movements?

Since Treismans theory, it has been generally accepted that color is an elementary feature that guides eye movements when looking at natural scenes. Hence, most computational models of visual attention predict eye movements using color as an important visual feature. In this paper, using experimental data, we show that color does not affect where observers look when viewing natural scene images. Neither colors nor abnormal colors modify observers fixation locations when compared to the same scenes in grayscale. In the same way, we did not find any significant difference between the scanpaths under grayscale, color, or abnormal color viewing conditions. However, we observed a decrease in fixation duration for color and abnormal color, and this was particularly true at the beginning of scene exploration. Finally, we found that abnormal color modifies saccade amplitude distribution.

Coreference Resolution for French Oral Data: Machine Learning Experiments with ANCOR

We present CROC (Coreference Resolution for Oral Corpus), the first machine learning system for coreference resolution in French. One specific aspect of the system is that it has been trained on data that come exclusively from transcribed speech, namely ANCOR (ANaphora and Coreference in ORal corpus), the first large-scale French corpus with anaphorical relation annotations. In its current state, the CROC system requires pre-annotated mentions. We detail the features used for the learning algorithms, and we present a set of experiments with these features. The scores we obtain are close to those of state-of-the-art systems for written English.