Geographic Information Extraction (GIE) involves the extraction of geo-referenced information from a data collection through steps of geoparsing and geocoding. The former is a process that starts from a free textual description of locations with the goal of identifying an unambiguous location, such as specific geographic coordinates expressed as latitude-longitude. Differently, geocoding regards the easier task of translating an exact and well-formatted location such as postal addresses. This paper presents MAWI, i.e. a pipeline that starts from generic texts about cities that first extracts geographic information to automatically detect possible points of interest, then generates textual snippets from their contexts by means of Natural Language Processing (NLP) techniques. The adopted methodology involves several modules, ranging from publicly available geocoding systems to NLP libraries for Named Entity Recognition and text segmentation. The impact of the proposal includes multiple tasks and applications, e.g. i) the enrichment of public platforms of geographic data, ii) the detection of geographic scopes in textual documents, iii) a geo-centric exploration of locations in the tourism domain, and so forth. In this contribution, we present an experimentation of the system with 50 input Wikipedia pages referring different cities, first demonstrating its effectiveness with a running example, then evaluating its power to detect and structure a highly-significant amount of novel geo-referenced information with respect to what currently encoded in Wikipedia. Data and code are publicly available for future research at https://anonymous.4open.science/r/PointOfInterest-8D80/.

In recent years, new digital technologies are being used to support the navigation and the analysis of scientific publications, justified by the increasing number of articles published every year. For this reason, experts make use of on-line systems to browse thousands of articles in search of relevant information. In this paper, we present a new method that automatically assigns meanings to references on the basis of the citation text through a Natural Language Processing pipeline and a slightly-supervised clustering process. The resulting network of semantically-linked articles allows an informed exploration of the research panorama through semantic paths. The proposed approach has been validated using the ACL Anthology Dataset containing several thousands of papers related to the Computational Linguistics field. A manual evaluation on the extracted citation meanings carried to very high levels of accuracy. Finally, a freely-available web-based application has been developed and published on-line.

Nowadays, many tools and systems are available to allow the analysis and the comparison of researchers' scientific production. The reason underlying such interest is evident: promotions, funding allocations, and employments are currently based on the evaluation (and direct comparisons) of publication lists. Existing measures, like H-index, aim at supporting this process by automatic calculations of quality and/or quantity indices. In this work, we propose a demonstration of a web environment, available at http://d-index.di.unito.it, that faces the problem of studying the impact of collaborations in research communities. The presented system allows the estimation of the impact of each scientific collaboration on the production of each researchers indexed by the DBLP bibliographic database by means of a novel time-based modeling of collaborative environments. The proposed application provides several interactions and visualization schemes to deeply discover clear and latent insights, over time, around the work of each researcher. Furthermore, it allows cross-community rankings of the authors depending on similar collaboration patterns and dependences.

Twitter is a popular microblogging service that acts as a ground-level information news flashes portal where people with different background, age, and social condition provide information about what is happening in front of their eyes. This characteristic makes Twitter probably the fastest information service in the world. In this article, we recognize this role of Twitter and propose a novel, user-aware topic detection technique that permits to retrieve, in real time, the most emerging topics of discussion expressed by the community within the interests of specific users. First, we analyze the topology of Twitter looking at how the information spreads over the network, taking into account the authority/influence of each active user. Then, we make use of a novel term aging model to compute the burstiness of each term, and provide a graph-based method to retrieve the minimal set of terms that can represent the corresponding topic. Finally, since any user can have topic preferences inferable from the shared content, we leverage such knowledge to highlight the most emerging topics within her foci of interest. As evaluation we then provide several experiments together with a user study proving the validity and reliability of the proposed approach.

In this work, we demonstrate a web application, available at http://d-index.di.unito.it, that permits to analyze the scientific profiles of all the researchers indexed by DBLP by focusing on the collaborations that contributed to define their curricula. The presented application allows the user to analyze the profile of a researcher, her dependence degrees on all the co-authors (along her entire scientific publication history) and to make comparisons among them in terms of dependence patterns. In particular, it is possible to estimate and visualize how much a researcher has benefited from collaboration with another researcher as well as the communities in which she has been involved. Moreover, the application permits to compare, in a single chart, each researcher with all the scientists indexed in DBLP by focusing on their dependences with respect to many other parameters like the total number of papers, the number of collaborations and the length of the scientific careers.

The high-dimensional nature of the textual data complicates the design of visualization tools to support exploration of large document corpora. In this article, we first argue that the Parallel Coordinates (PC) technique, which can map multidimensional vectors onto a 2D space in such a way that elements with similar values are represented as similar poly-lines or curves in the visualization space, can be used to help users discern patterns in document collections. The inherent reduction in dimensionality during the mapping from multidimensional points to 2D lines, however, may result in visual complications. For instance, the lines that correspond to clusters of objects that are separate in the multidimensional space may overlap each other in the 2D space; the resulting increase in the number of crossings would make it hard to distinguish the individual document clusters. Such crossings of lines and overly dense regions are significant sources of visual clutter, thus avoiding them may help interpret the visualization. In this article, we note that visual clutter can be significantly reduced by adjusting the resolution of the individual term coordinates by clustering the corresponding values. Such reductions in the resolution of the individual term-coordinates, however, will lead to a certain degree of information loss and thus the appropriate resolution for the term-coordinates has to be selected carefully. Thus, in this article we propose a controlled clutter reduction approach, called Parallel hierarchical Coordinates (or PhC ), for reducing the visual clutter in PC-based visualizations of text corpora. We define visual clutter and information loss measures and provide extensive evaluations that show that the proposed PhC provides significant visual gains (i.e., multiple orders of reductions in visual clutter) with small information loss during visualization and exploration of document collections.

We present the pattern development view (pDView) system for multidimensional scientific data visualization. The pDView system relies on a novel pattern development tree (pDTree) structure to unravel patterns in multidimensional data without having to rely on visualizations that require either significant degrees of projections that eliminate certain dimensions at the expense of the others or introduce significant visual overhead due to overly-rich multi-dimensional graphic interfaces. Instead, pDView maps data along all its relevant dimensions onto a pDTree structure, capturing and visualizing the underlying fundamental relationships. The user is able to vary contextual parameters to observe the strength and robustness of these relationships under different situations.

We present the tagFlake system, which supports semantically informed navigation within a tag cloud. tagFlake relies on TMine for organizing tags extracted from textual content in hierarchical organizations, suitable for navigation, visualization, classification, and tracking. TMine extracts the most significant tag/terms from text documents and maps them onto a hierarchy in such a way that descendant terms are contextually dependent on their ancestors within the given corpus of documents. This provides tagFlake with a mechanism for enabling navigation within the tag space and for classification of the text documents based on the contextual structure captured by the created hierarchy. tagFlake is language neutral, since it does not rely on any natural language processing technique and is unsupervised.