Cognitive Reserve and Its Effect in Older Adults on Retrieval of Proper Names, Logo Names and Common Nouns

Previous studies showed that high Cognitive Reserve (CR, years of education and experience and knowledge acquired in life) is correlated with language proficiency as measured with vocabulary size, verbal analogy, and semantic processing. The aim of the present study is to investigate the relationship between CR and the ability in retrieving different categories of words: Proper Names, Logo Names, and Common Nouns. The hypothesis is that CR contributes more in retrieving Common Nouns and Logo Names which are highly semantically interconnected, than retrieving Proper Names which are pure referring expressions. Forty-six Italian healthy older adults underwent the Montreal Cognitive Assessment (MoCA) and their performances spanned from low to high global cognitive profile. They were also administered a picture naming task for Proper Names, Logo Names and Common Nouns. Latency and Accuracy were recorded. CR was measured with the Cognitive Reserve Index (CRI) questionnaire which provides a measure of education, working time activities, and leisure time activities. Participants were significantly faster and more accurate in name retrieval when CR was high. CRI and MoCA as interaction terms predicted naming Latency with a stronger effect of CRI when the global cognitive profile was in the low range. The effect of CRI on Accuracy was lower for Proper Names than for Common Nouns and Logo Names, which did not differ from each other. Our results show that name retrieval Accuracy can be predicted by CR, significantly more in the case of Logo Names and Common Nouns than in the case of Proper Names. As Proper Names have scarce semantic interconnections and are arbitrarily assigned to unique individuals, they are not much influenced by CR. Although Logo Names are also arbitrarily assigned to their bearers, they can be conceptually categorized and thus influenced by reserve. The weak relationship between Proper Names and CR might suggest a proper name task as a useful tool to detect early signs of dementia, in particular for persons with high CR

Unnamed locations, underspecified regions, and other linguistic phenomena in geographic annotation of water-based locations

This short paper investigates how locations in or close to water masses in topics and documents (e.g. rivers, seas, oceans) are referred to. For this study, 13 topics from the GeoCLEF topics 2005-2008 aiming at documents on rivers, oceans, or sea names were selected and the corresponding relevant documents retrieved and manually annotated. Results of the geographic annotation indicate that i) topics aiming at locations close to water contain a wide variety of spatial relations (indicated by dierent prepositions), ii) unnamed locations can be generated on-the-fly by referring to movable objects (e.g. ships, planes) travelling along a path, iii) underspecied regions are referenced by proximity or distance or directional relations. In addition, several generic expressions (e.g. "in international waters") are frequently used, but refer to different underspecified regions

A Novelty-based Evaluation Method for Information Retrieval

In information retrieval research, precision and recall have long been used to evaluate IR systems. However, given that a number of retrieval systems resembling one another are already available to the public, it is valuable to retrieve novel relevant documents, i.e., documents that cannot be retrieved by those existing systems. In view of this problem, we propose an evaluation method that favors systems retrieving as many novel documents as possible. We also used our method to evaluate systems that participated in the IREX workshop.Comment: 5 page

Target Type Identification for Entity-Bearing Queries

Identifying the target types of entity-bearing queries can help improve retrieval performance as well as the overall search experience. In this work, we address the problem of automatically detecting the target types of a query with respect to a type taxonomy. We propose a supervised learning approach with a rich variety of features. Using a purpose-built test collection, we show that our approach outperforms existing methods by a remarkable margin. This is an extended version of the article published with the same title in the Proceedings of SIGIR'17.Comment: Extended version of SIGIR'17 short paper, 5 page

Encoding Classifications as Lightweight Ontologies

Classifications have been used for centuries with the goal of cataloguing and searching large sets of objects. In the early days it was mainly books; lately it has also become Web pages, pictures and any kind of electronic information items. Classifications describe their contents using natural language labels, which has proved very effective in manual classification. However natural language labels show their limitations when one tries to automate the process, as they make it very hard to reason about classifications and their contents. In this paper we introduce the novel notion of Formal Classification, as a graph structure where labels are written in a propositional concept language. Formal Classifications turn out to be some form of lightweight ontologies. This, in turn, allows us to reason about them, to associate to each node a normal form formula which univocally describes its contents, and to reduce document classification to reasoning about subsumption

Beyond English text: Multilingual and multimedia information retrieval.

Non