209 research outputs found
Napoli all'esposizione generale italiana in Torino nel 1884
No full textNapoli all'esposizione generale italiana in Torino nel 1884 / Roberto Moschitti. - Napoli : Tip. Commerciale, 1884. - 381 p. ; 18 c
A.: On reverse feature engineering of syntactic tree kernels
No full textIn this paper, we provide a theoretical framework for feature selection in tree ker-nel spaces based on gradient-vector com-ponents of kernel-based machines. We show that a huge number of features can be discarded without a significant decrease in accuracy. Our selection algorithm is as accurate as and much more efficient than those proposed in previous work. Com-parative experiments on three interesting and very diverse classification tasks, i.e. Question Classification, Relation Extrac-tion and Semantic Role Labeling, support our theoretical findings and demonstrate the algorithm performance.
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
Automatic learning of textual entailments with cross-pair similarities
No full textIn this paper we define a novel similarity
measure between examples of textual entailments and we use it as a kernel function in Support Vector Machines (SVMs).
This allows us to automatically learn the
rewrite rules that describe a non trivial set
of entailment cases. The experiments with
the data sets of the RTE 2005 challenge
show an improvement of 4.4% over the state-of-the-art methods
Experimenting a "general purpose" textual entailment learner in AVE
No full textIn this paper we present the use of a "general purpose" textual entailment recognizer in the Answer Validation Exercise (AVE) task. Our system is designed to learn entailment rules from annotated examples. Its main feature is the use of Support Vector Machines (SVMs) with kernel functions based on cross-pair similarity between entailment pairs. We experimented with our system using different training sets: RTE and AVE data sets. The comparative results show that entailment rules can be learned. Although, the high variability of the outcome prevents us to derive definitive conclusions, the results show that our approach is quite promising and improvable in the future. © Springer-Verlag Berlin Heidelberg 2007
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
Fast and Effective Kernels for Relational Learning from Texts
No full textIn this paper, we define a family of syntactic kernels for automatic relational learning from pairs of natural language sentences. We provide an efficient computation of such models by optimizing the dynamic programming algorithm of the kernel evaluation. Experiments with Support Vector Machines and the above kernels show the effectiveness and efficiency of our approach on two very important natural language tasks, Textual Entailment Recognition and Question Answering. 1
A Machine learning approach to textual entailment recognition
No full textDesigning models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so
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