Bilingual episodic memory: an introduction

Our current models of bilingual memory are essentially accounts of semantic memory whose goal is to explain bilingual lexical access to underlying imagistic and conceptual referents. While this research has included episodic memory, it has focused largely on recall for words, phrases, and sentences in the service of understanding the structure of semantic memory. Building on the four papers in this special issue, this article focuses on larger units of episodic memory(from quotidian events with simple narrative form to complex autobiographical memories) in service of developing a model of bilingual episodic memory. This requires integrating theory and research on how culture-specific narrative traditions inform encoding and retrieval with theory and research on the relation between(monolingual) semantic and episodic memory(Schank, 1982; Schank & Abelson, 1995; Tulving, 2002). Then, taking a cue from memory-based text processing studies in psycholinguistics(McKoon & Ratcliff, 1998), we suggest that as language forms surface in the progressive retrieval of features of an event, they trigger further forms within the same language serving to guide a within-language/ within-culture retrieval

Challenges for chemical information extraction and text retrieval

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Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis

We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules. On the one hand, we exploit some of the main advantages of unsupervised algorithms: (1) the interpretability of their output, in contrast with most supervised models, which behave as a black box and (2) their robustness across different corpora and domains. On the other hand, by introducing the concept of compositional operations and exploiting syntactic information in the form of universal dependencies, we tackle one of their main drawbacks: their rigidity on data that are structured differently depending on the language concerned. Experiments show an improvement both over existing unsupervised methods, and over state-of-the-art supervised models when evaluating outside their corpus of origin. Experiments also show how the same compositional operations can be shared across languages. The system is available at http://www.grupolys.org/software/UUUSA/Comment: 19 pages, 5 Tables, 6 Figures. This is the authors version of a work that was accepted for publication in Knowledge-Based System

Benchmarking News Recommendations in a Living Lab

Most user-centric studies of information access systems in literature suffer from unrealistic settings or limited numbers of users who participate in the study. In order to address this issue, the idea of a living lab has been promoted. Living labs allow us to evaluate research hypotheses using a large number of users who satisfy their information need in a real context. In this paper, we introduce a living lab on news recommendation in real time. The living lab has first been organized as News Recommendation Challenge at ACM RecSys’13 and then as campaign-style evaluation lab NEWSREEL at CLEF’14. Within this lab, researchers were asked to provide news article recommendations to millions of users in real time. Different from user studies which have been performed in a laboratory, these users are following their own agenda. Consequently, laboratory bias on their behavior can be neglected. We outline the living lab scenario and the experimental setup of the two benchmarking events. We argue that the living lab can serve as reference point for the implementation of living labs for the evaluation of information access systems