1,721,043 research outputs found
Land Use Classification with Point of Interests and Structural Patterns
No full textIn this paper, we present a framework for performing automatic analysis of Land Use Zones based on Location-Based Social Networks (LBSNs). We model city areas using a hierarchical structure of POIs extracted from foursquare. We encode such structures in kernel machines, e.g., Support Vector Machines, using a new Tree Kernel, i.e., the Hierarchical POI Kernel (HPK), which can take the importance of the individual POIs into account during the substructure matching. This way, HPK projects structures in the space of all their possible substructures such that each dimension corresponds to a semantic structural feature, weighted according to the discriminative power of POIs . We generated four different datasets for the following cities: Barcelona, Lisbon, Amsterdam and Milan, where we trained and tested our models. The results show that our approach largely outperforms previous work and standard baseline built on simple features, such as counts of different POIs. Finally, we apply a mining algorithm to extract the most relevant features (tree fragments) from the implicit TK space according to the weights the kernel machine assigned to them. Our approach can produce an explicit set of representative features that can be used to classify and characterize urban areas
Harmonization and development of resources and tools for Italian natural language processing within the PARLI project
No full textThe papers collected in this volume are selected as a sample of the progress in Natural Language Processing (NLP) performed within the Italian NLP community and especially attested by the PARLI project. PARLI (Portale per l’Accesso alle Risorse in Lingua Italiana) is a project partially funded by the Ministero Italiano per l’Università e la Ricerca (PRIN 2008) from 2008 to 2012 for monitoring and fostering the harmonic growth and coordination of the activities of Italian NLP. It was proposed by various teams of researchers working in Italian universities and research institutions. According to the spirit of the PARLI project, most of the resources and tools created within the project and here described are freely distributed and they did not terminate their life at the end of the project itself, hoping they could be a key factor in future development of computational linguistics
SenTube: A Corpus for Sentiment Analysis on YouTube Social Media
No full textIn this paper we present SenTube -- a dataset of user-generated comments on YouTube videos annotated for information content and sentiment polarity. It contains annotations that allow to develop classifiers for several important NLP tasks: (i) sentiment analysis, (ii) text categorization (relatedness of a comment to video and/or product), (iii) spam detection, and (iv) prediction of comment informativeness. The SenTube corpus favors the development of research on indexing and searching YouTube videos exploiting information derived from comments. The corpus will cover several languages: at the moment, we focus on English and Italian, with Spanish and Dutch parts scheduled for the later stages of the project. For all the languages, we collect videos for the same set of products, thus offering possibilities for multi- and cross-lingual experiments. The paper provides annotation guidelines, corpus statistics and annotator agreement details
Efficient Online Learning for Mapping Kernels on Linguistic Structures
Full text linkKernel methods are popular and effective techniques for learn- ing on structured data, such as trees and graphs. One of their major drawbacks is the computational cost related to making a prediction on an example, which manifests in the classifica- tion phase for batch kernel methods, and especially in online learning algorithms. In this paper, we analyze how to speed up the prediction when the kernel function is an instance of the Mapping Kernels, a general framework for specifying ker- nels for structured data which extends the popular convolution kernel framework. We theoretically study the general model, derive various optimization strategies and show how to apply them to popular kernels for structured data. Additionally, we derive a reliable empirical evidence on semantic role labeling task, which is a natural language classification task, highly dependent on syntactic trees. The results show that our faster approach can clearly improve on standard kernel-based SVMs, which cannot run on very large datasets
Structural Semantic Models for Automatic Analysis of Land Use
No full textThe growing availability of data from cities (e.g., traffic flow, human mobility and geographical data) open new opportunities for predicting and thus optimizing human activities. For example, the automatic analysis of land use enables the possibility of better administrating a city in terms of resources and provided services. However, such analysis requires specific information, which is often not available for privacy concerns. In this paper, we propose a novel machine learning representation based on the available public information to classify the most predominant land use of an urban area, which is a very common task in urban computing. In particular, in addition to standard feature vectors, we encode geo-social data from Location-Based Social Networks (LBSNs) into a conceptual tree structure that we call Geo-Tree. Then, we use such representation in kernel machines, which can thus perform accurate classification exploiting hierarchical substructure of concepts as features. Our extensive comparative study on the areas of New York and its boroughs shows that Tree Kernels applied to Geo-Trees are very effective improving the state of the art up to 18% in Macro-F1
Exploring the structure of BERT through Kernel Learning
No full textCombining internal representations of a pre-trained Transformer model, such as the popular BERT, is an interesting and challenging task nowadays. Usually, internal representations are combined by simple heuristics, e.g. concatenation or average of a subset of layers, with a consequent need for calibrating multiple hyper-parameters during the fine-tuning phase. Inspired by the recent literature, we propose a principled approach to optimally combine internal representations of a Transformer model via Multiple Kernel Learning strategies. Broadly speaking, the proposed system consists of two elements. The former is a canonical Transformer model fine-tuned on the target task. The latter is a Multiple Kernel Learning algorithm that extracts and combines representations developed in the internal layers of the Transformer and performs predictions. Most important, we use the system as a powerful tool to inspect the information encoded into the Transformer network, emphasizing the limits of state-of-the-art models
EvalIta 2011: The frame labeling over Italian texts task
No full textThe Frame Labeling over Italian Texts (FLaIT) task held within the EvalIta 2011 challenge is here described. It focuses on the automatic annotation of free texts according to frame semantics. Systems were asked to label all semantic frames and their arguments, as evoked by predicate words occurring in plain text sentences. Proposed systems are based on a variety of learning techniques and achieve very good results, over 80% of accuracy, in most subtasks. © Springer-Verlag Berlin Heidelberg 2013
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
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