1,721,113 research outputs found
A Multimodal Framework for Recognizing Emotional Feedback in Conversational Recommender Systems.
No full textA general architecture for an emotion-aware content-based recommender system
No full textEmotions play a crucial role in the decision making process.
Frequently, choices are strongly influenced by the mood of the moment, and the same person could take different de-
cisions at dierent time on the same topic. Recommender systems, that are denitively recognized as tools for supporting the decision making process, demonstrated to be more accurate exploiting emotive labels in several work. For this reason a large number of researchers are focusing their attention on the analysis of the emotions by exploiting data that users daily disseminate on the Web (e.g.: Social Networks, Blogs, Forums, etc.). In this paper we propose a general architecture for implementing an emotion-aware content-based recommender system. Furthermore, we developed a web service that researchers can freely exploit for their own implementations. We carried out a user study on the domain of music recommendation, particularly influenced by the user emotion, and results are very promising
Extracting Relations from Italian Wikipedia using Self-Training
No full textThis dataset contains relations extracted from the Italian Wikipedia by the WikiOIE framework.
WikiOIE is based on UDPipe and the Universal Dependencies project for text processing.
It easily allows customizing the information extraction (IE) approach to automatically extract triples (subject, predicate, object).
This dataset contains relations extracted by a supervised approach based on self-training.
The extraction process is provided in JSON format.
Version 2 of the dataset was extracted using an improved version of the learning algorithm. The files of version 2 are identified by the suffix "_reg" in the file name.
More information and the Java code are available here: https://github.com/pippokill/WikiOIE
Self-training approach:
Lucia Siciliani, Pierluigi Cassotti, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro 2021. Extracting Relations from Italian Wikipedia using Self-Training. In Proceedings of the Eighth Italian Conference on Computational Linguistics (CLiC-it 2021). CEUR-WS.
WikiOIE framework:
Pierluigi Cassotti, Lucia Siciliani, Pierpaolo Basile, Marco de Gemmis, and Pasquale Lops. 2021. Extracting relations from Italian Wikipedia using unsupervised information extraction. In Proceedings of the 11th Italian Information Retrieval Workshop 2021 (IIR 2021). CEUR-WS
Visualizzazione del cambiamento d’uso del maschile e femminile nei titoli occupazionali
No full textIn questo lavoro presentiamo uno strumento per la visualizzazione di statistiche riguardanti l’uso delle forme grammaticali
maschile e femminile di titoli occupazionali e dei professionisti menzionati contestualmente ai titoli occupazioni in un
corpus diacronico. Le statistiche sono state calcolate utilizzando un corpus diacronico di articoli estratti da quotidiani
italiani, composto da 3.5 miliardi di tokens. Le occorrenze dei titoli occupazionali sono state filtrate per ridurre rumore
introdotto dalla polisemia dei termini. L’interfaccia web permette un uso semplice ed intuitivo grazie all’utilizzo di
tecnologie allo stato dell’arte per la visualizzazione di grafici. L’interfaccia offre la possibilità di visualizzare, confrontare
e analizzare le serie temporali delle frequenze relative dei titoli occupazionali nella forma maschile e femminile e delle
frequenze assolute delle occorrenze dei professionisti menzionati nel testo
Deep content analytics methods to improve transparency and serendipity of recommender systems
No full textAdvanced methods for Natural Language Processing and the availability of open knowledge sources, such as Wikipedia and BabelNet, have promoted recent progress in the field of content-based recommender systems (CBRSs).
Those systems analyze both item descriptions (content) and user ratings to infer user profiles, which store information about preferences, exploited to suggest items similar to those users liked in the past.
Novel research works have introduced Deep Content Analytics methods, i.e. semantic techniques that allow a better understanding of item properties, in terms of concepts instead of keywords. CBRSs can benefit from these techniques to implement more advanced, meaningful representations of items and user profiles.
The talk will provide a basic survey of semantic techniques:
top-down approaches, based on the use of different open knowledge sources (ontologies, Wikipedia, DBpedia, BabelNet)
bottom-up approaches, based on the distributional hypothesis, which states that "words that occur in the same contexts tend to have similar meanings".
The talk will show a semantic approach designed to provide explanations of suggestions, and a method for the discovery of hidden correlations among items, exploited to find “serendipitous” recommendations, i.e. items which are unexpected and potentially interesting at the same time
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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