1,720,995 research outputs found
TaxoSBERT: Unsupervised Taxonomy Expansion Through Expressive Semantic Similarity
No full textKnowledge graphs are crucial resources for a large set of document management tasks, such as text retrieval and classification as well as natural language inference. Standard examples are large-scale lexical semantic graphs, such as WordNet, useful for text tagging or sentence disambiguation purposes. The dynamics of lexical taxonomies is a critical problem as they need to be maintained to follow the language evolution across time. Taxonomy expansion, in this sense, becomes a critical semantic task, as it allows for an extension of existing resources with new properties but also to create new entries, i.e. taxonomy concepts, when necessary. Previous work on this topic suggests the use of neural learning methods able to make use of the underlying taxonomy graph as a source of training evidence. This can be done by graph-based learning, where nets are trained to encode the underlying knowledge graph and to predict appropriate inferences. This paper presents TaxoSBERT as a simple and effective way to model the taxonomy expansion problem as a retrieval task. It combines a robust semantic similarity measure and taxonomy-driven re-rank strategies. This method is unsupervised, the adopted similarity measures are trained on (large-scale) resources out of a target taxonomy and are extremely efficient. The experimental evaluation with respect to two taxonomies shows surprising results, improving far more complex state-of-the-art methods
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
Business Knowledge and Neural Learning: organisation-specific transformer via semantic pre-training
No full textAI approaches to business knowledge management have often neglected the role of documents, which are the backbone of expertise, norms, and optimal practices that every organisation implicitly encodes in its large-scale document collections. Banks make no exception and have to deal with operational documents on business process engineering, as well as norms on legal compliance aspects. They are thus particularly interested in the mining of the huge body of knowledge implicitly stored in their text archives, i.e. in their document assets. Extracting semantic metadata from raw bank documents is therefore central for supporting effective governance, business engineering as well as legal monitoring processes in an accurate and profitable manner. In this paper, a weakly-supervised neural methodology for creating semantic metadata from bank documents and its application to different banking organisations is presented. Based on a neural pre-training methodology driven by knowledge models of individual banks, it is shown to improve with respect to inductive approaches previously presented, that are domain specific, but organisation independent. The application to business process design in different Italian banks has been here tested and the observed impact through measurements confirms its wide applicability at the level of banks, as well as to other business organisations
An end-to-end transformer-based model for interactive grounded language understanding
No full textThis paper delves into Interactive Grounded Language Understanding (IGLU) problems within the context of Human-Robot Interaction (HRI), where a robot interprets user commands about the environment. In this scenario, the robot's objective is to determine if a given command can be executed within the environment. If ambiguity or incomplete information is detected, the robot generates pertinent clarification questions. Drawing inspiration from the GrUT framework and employing a BART-based model that combines the user's utterance with the description of the environment, this study evaluates the applicability of the GrUT approach in an end-to-end Grounded QG task. The assessment of question quality is conducted through both automated metrics and human evaluation. While the results highlight the proficiency of the BART-based method in question generation, challenges arise due to dataset limitations from the IGLU competition at NeurIPS 2022. Nevertheless, this research provides valuable insights into BART's generative capabilities in the realm of HRI
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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Knowledge-Based Neural Pre-training for Intelligent Document Management
No full textBanks are usually large and complex companies that face a number of challenges to support the rapid and effective sharing of information and content across their organizations. Extracting complex metadata from raw bank documents is therefore central to support intelligent data indexing, information circulation and to promote more complex predictive capabilities, e.g., compliance assessment problems. In this paper, we present a weakly-supervised neural methodology for creating semantic metadata from bank documents. It exploits a neural pre-training method optimized against legacy semantic resources able to minimize the training effort. We studied an application to business process design and management in banks and tested the method on documents from the Italian banking community. The measured impact of the proposed training approach to process-related metadata creation confirms its applicability
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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