Conceptual Abstractness: from Nouns to Verbs

Investigating lexical access, representation and processing involves dealing with conceptual abstractness: abstract concepts are known to be more quickly and easily delivered in human communications than abstract meanings (Binder et al., 2005). Although these aspects have long been left unexplored, they are relevant: abstract terms are widespread in ordinary language, as they contribute to the realisation of various sorts of figurative language (metaphors, metonymies, hyperboles, etc.). Abstractness is therefore an issue for computational linguistics, as well. In this paper we illustrate how to characterise verbs with abstractness information. We provide an experimental evaluation of the presented approach on the largest existing corpus annotated with abstraction scores: our results exhibit good correlation with human ratings, and point out some open issues that will be addressed in future work.In questo lavoro presentiamo il tema dell’astrattezza come una caratteristica diffusa del linguaggio, e un nodo cruciale nell’elaborazione automatica del linguaggio. In particolare illustriamo un metodo per la stima dell’astrattezza che caratterizza i verbi a partire dalla composizione dei punteggi di astrattezza degli argomenti dei verbi utilizzando la risorsa Abs-COVER

Query Difficulty Prediction for Contextual Image Retrieval

Abstract. This paper explores how to predict query difficulty for contextual image retrieval. We reformulate the problem as the task of predicting how difficult to represent a query as images. We propose to use machine learning algorithms to learn the query difficulty prediction models based on the characteristics of the query words as well as the query context. More specifically, we focus on noun word/phrase queries and propose four features based on several assumptions. We created an evaluation data set by hand and compare several machine learning algorithms on the prediction task. Our preliminary experimental results show the effectiveness of our proposed features and the stable performance using different classification models. Key words: Query difficulty, Contextual image retrieval

Predição de relevância em sistemas de recuperação de informação

Orientador: Anderson de Rezende RochaTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: No mundo conectado atual, Recuperação de Informação (IR) tem se tornado um campo de pesquisa de crescente interesse, sendo um problema presente em muitas aplicações modernas. Dentre os muitos desafios no desenvolvimento the sistemas de IR está uma correta avaliação de performance desses sistemas. Avaliação \emph{offline}, entretanto, se limita na maioria dos casos ao \emph{benchamark} e comparação de performance entre diferentes sistemas. Esse fato levou ao surgimento do problema denomidado Predição de Performance de Consulta (QPP), cujo objetivo é estimar, em tempo de consulta, a qualidade dos resultados obtidos. Nos últimos anos, QPP recebeu grande atenção na literatura, sobretudo no contexto de busca textual. Ainda assim, QPP também tem suas limitações, principalmente por ser uma maneira indireta de estimar a performance de sistemas de IR. Nessa tese, investigamos formular o problema de QPP como um problema de \emph{predição de relevância}: a tarefa de predizer, para um determinado \topk, quais resultados de uma consulta são de fato relevantes para ela, de acordo com uma referência de relevância existente. Apesar de notavelmente desafiador, predição de relevância é não só uma maneira mais natural de estimar performance, como também com diversas aplicações. Nessa tese, apresentamos três famílias de métodos de predição de relevância: estatísticos, aprendizado, e rotulação sequencial. Todos os métodos nessas famílias tiveram sua efetividade avaliada em diversos experimentos em recuperação de imagens por conteúdo, cobrindo uma vasta gama de conjuntos de dados de grande-escala, assim como diferentes configurações de recuperação. Mostramos que é possível gerar predições de relevância precisas, para grandes valores de $k$, não só connhecendo pouco do sistema de IR analisado, como também de forma eficiente o bastante para ser aplicável em tempo de consulta. Finalizamos esta tese discutindo alguns caminhos possíveis para melhorar os resultados obtidos, assim como trabalhos futuros nesse campo de pesquisaAbstract: In today¿s connected world, Information Retrieval (IR) has become one of the most ubiquitous problems, being part of many modern applications. Among all challenges in designing IR systems, how to evaluate their performance is ever-present. Offline evaluation, however, is mostly limited to benchmarking and comparison of different systems, which has pushed a growing interest in predicting, at query time, the performance of an IR system. Query Performance Prediction (QPP) is the name given to the problem of estimating the quality of results retrieved by an IR system in response to a query. In the past few years, this problem received much attention, especially by the text retrieval community. Yet, QPP is still limited as only an indirect way of estimating the performance of IR systems. In this thesis, we investigate formulating the QPP problem as a \emph{relevance prediction} one: the task of predicting, for a specific \topk, which results of a query are relevant to it, according to some existing relevance reference. Though remarkably challenging, relevance prediction is not only a more natural way of predicting performance but also one with significantly more applications. In this thesis, we present three families of relevance prediction approaches: statistical, learning, and sequence labeling. All methods within those families are evaluated concerning their effectiveness in several content-based image retrieval experiments, covering several large-scale datasets and retrieval settings. The experiments in this thesis show that it is feasible to perform relevance prediction for $k$ values as large as 30, with minimal information about the underlying IR system, and efficiently enough to be performed at query time. This thesis is concluded by offering some potential paths for improving the current results, as well as future research in this particular fieldDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação168326/2017-5CAPESCNP

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges