UNLABELLED The Summary Tree Explorer (STE) is a Java application for interactively exploring sets of phylogenetic trees using two coupled representations: a node-and-link diagram and a textual list of common clades. Selection, pruning, filtering or re-rooting in one representation is immediately reflected in the other. While summary trees are more effective at showing the relationship among cla...

In this work, we propose an approach to index Deep Convolutional Neural Network Features to support efficient content-based retrieval on large image databases. To this aim, we have converted the these features into a textual form, to index them into an inverted index by means of Lucene. In this way, we were able to set up a robust retrieval system that combines full-text search with content-bas...

This paper addresses the semantic issue of exceptions in natural language texts. After observing that rst-order logic is not adequate for semantic representations of natural language , we make use of extended logic programs for meaning representation and inference engine in natural language processing. We show how to generate semantics of texts through deductive parsing by using deenite clause ...

The paper reminds us that there has been a long history of mutual influence between ethnography and aesthetics. There is nothing new or recent in textual or graphic experimentation inspired by anthropological or sociological fieldwork. We have not had to wait for the so-called crisis of representation to acknowledge this. Anthropology was among the direct sources and inspirations for modernist ...

The Interactive Strategy Trainer for Active Reading and Thinking (iSTART) is an intelligent tutoring system that provides students with automated training on reading strategies. In particular, iSTART trains students to selfexplain target sentences so as to integrate encoded information into a coherent mental representation. The goal of this study was to investigate the relation between text str...

Disambiguating named entities (NE) in running text to their correct interpretations in a specific knowledge base (KB) is an important problem in NLP. This paper presents two collective disambiguation approaches using a graph representation where possible KB candidates for NE textual mentions are represented as nodes and the coherence relations between different NE candidates are represented by ...

Data pre-processing is an important topic in Text Classification (TC). It aims to convert the original textual data in a data-mining-ready structure, where the most significant text-features that serve to differentiate between textcategories are identified. Broadly speaking, textual data pre-processing techniques can be divided into three groups: (i) linguistic, (ii) statistical, and (iii) hybr...

Unstructured textual data such as students’ essays and life narratives can provide helpful information in educational and psychological measurement, but often contain irregularities and ambiguities, which creates difficulties in analysis. Text mining techniques that seek to extract useful information from textual data sources through identifying interesting patterns are promising. This chapter ...

Cross language-image retrieval is a problem of high interest that is at the frontier between computer vision and natural language processing. State-of-the-art methods learn a common space with regard to some constraints of correlation or similarity from two textual and visual modalities that are processed in parallel and possibly jointly. This paper proposes a different approach that considers ...

This paper introduces GAF, a grounded annotation framework to represent events in a formal context that can represent information from both textual and extra-textual sources. GAF makes a clear distinction between mentions of events in text and their formal representation as instances in a semantic layer. Instances are represented by RDF compliant URIs that are shared across different research d...