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Ana García-Serrano

Researcher at National University of Distance Education

Publications -  113
Citations -  1198

Ana García-Serrano is an academic researcher from National University of Distance Education. The author has contributed to research in topics: Semantic similarity & Decision support system. The author has an hindex of 18, co-authored 107 publications receiving 1071 citations. Previous affiliations of Ana García-Serrano include Polytechnic University of Puerto Rico & Complutense University of Madrid.

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Multiagent architectures for intelligent traffic management systems

TL;DR: This paper describes and compares integrated TRYS and TRYS autonomous agents, two multiagent systems that perform decision support for real-time traffic management in the urban motorway network around Barcelona, and develops some conclusions respecting the general applicability of multiagent architectures for intelligent traffic management.
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A reproducible survey on word embeddings and ontology-based methods for word similarity: Linear combinations outperform the state of the art

TL;DR: This work introduces the largest, reproducible and detailed experimental survey of OM measures and THE AUTHORS models reported in the literature, based on the evaluation of both families of methods on a same software platform, with the aim of elucidating what is the state of the problem.
Proceedings Article

Q-WordNet: Extracting Polarity from WordNet Senses

TL;DR: Q-WordNet, a lexical resource consisting of WordNet senses automatically annotated by positive and negative polarity, can be used as a starting point for data-driven approaches in sentiment analysis and shows an improvement with respect to previous approaches.
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Decision support for traffic management based on organisational and communicative multiagent abstractions

TL;DR: This paper outlines a design method for the construction of agent-based DSS from an organisational and communicative model of decision support environments and presents an abstract.
Journal ArticleDOI

HESML: A scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset

TL;DR: PosetHERep proposes a memory-efficient representation for taxonomies which linearly scales with the size of the taxonomy and provides an efficient implementation of most taxonomy-based algorithms used by the semantic measures and IC models, whilst HESML provides an open framework to aid research into the area by providing a simpler and more efficient software architecture than the current software libraries.