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ldk 2021 - Proceedings of the workshops and tutorials - language data and knowledge, co-located with the language, data and knowledge Conference, ldk 2021

LDK 2021 - Proceedings of the Workshops and Tutorials - Lang...

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2021 workshops and tutorials - language data and knowledge, ldk 2021

The proceedings contain 14 papers. The topics discussed include: nose-first. towards an olfactory gaze for digital art history;sentiment analysis meets linguistic linked data: an overview of the state of the art;extending general sentiment lexicon to specific domains in (semi-)automatic manner;multilingual knowledge systems as linguistic linked open data for European language grid;LOD-connected offensive language ontology and tagset enrichment;extending and using a sentiment lexicon for Latin in a linked data framework;aspect-based sentiment analysis of conference review forms with LD-enabled review criteria;and from meaning to perception - exploring the space between word and odor perception embeddings.

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Towards Visual Federated SPARQL Queries 20

Towards Visual Federated SPARQL Queries

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Joint of Posters, Demos, workshops, and tutorials of the 20th International Conference on Semantic Systems, SEMANTiCS-PDWT 2024

作者： Čerāns, Kārlis Bojārs, Uldis Ovčiņņikova, Jūlija Lāce, Lelde Grasmanis, Mikus Sproģis, Artūrs Institute of Mathematics and Computer Science University of Latvia Riga Latvia

We demonstrate a method for visual creation of schema-backed federated queries that features schema summary visualizations and context-aware auto-completion of queries, based on schemas of multiple data *** method is implemented in the context of the ViziQuer query tool, based on the collection of multiple stored data schemas, including schemas for DBpedia and *** environment for schema visualization and for the creation of visual federated queries is available as an online playground and as an open-source software for local installation. © 2024 Copyright for this paper by its authors.

关键词： Structured Query language

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Extending and Using a Sentiment Lexicon for Latin in a Linked data Framework

Extending and Using a Sentiment Lexicon for Latin in a Linke...

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2021 workshops and tutorials - language data and knowledge, ldk 2021

作者： Sprugnoli, Rachele Passarotti, Marco Testori, Marinella Moretti, Giovanni CIRCSE Research Centre Università Cattolica del Sacro Cuore Milan Italy

In this paper we present the methodology followed to extend a Latin sentiment lexicon (called LatinAffectus), the process of inclusion of the lexicon in a knowledge base of interoperable linguistic resources for Latin and one use case performed on the treebank of Dante Alighieri's Latin works annotated following the Universal Dependencies guidelines. In addition, we report on our first attempt at linking the polarity scores of SentiWordNet 3.0 to a manually revised version of Latin WordNet. © 2021 Copyright for this paper by its authors

关键词： Linked data

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Sentiment Analysis meets Linguistic Linked data: An overview of the State of the Art

Sentiment Analysis meets Linguistic Linked Data: An overview...

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2021 workshops and tutorials - language data and knowledge, ldk 2021

作者： Iglesias, Carlos Á. Sánchez-Rada, J. Fernando Intelligent Systems Group Universidad Politécnica de Madrid Spain

Sentiment Analysis has received plenty of attention from both industry and academia because its application can reveal new insights from social interactions. The wide range of final users of these services includes public services, businesses, and individuals. Linked data technologies provide an effective and seamless way for integrating services and interlinking language resources. This paper provides an introduction to the main approaches, applications, and datasets. © 2021 Copyright for this paper by its authors

关键词： Sentiment analysis

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The DBpedia Technology Tutorial

The DBpedia Technology Tutorial

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2021 workshops and tutorials - language data and knowledge, ldk 2021

作者： Dojchinovski, Milan Forberg, Jan Frey, Johannes Hofer, Marvin Streitmatter, Denis Yankov, Kirill DBpedia Association/InfAI Leipzig University Leipzig Germany Web Intelligence Research Group FIT Czech Technical University in Prague Prague Czech Republic

DBpedia (https://***) is a crowd-sourced community effort which aims at extraction and publishing structured information from various Wikimedia projects. This structured information resembles an open knowledge graph, the DBpedia knowledge Graph, which is publicly available for everyone on the Web. The DBpedia knowledge Graph has been under development for many years and is being improved to this day. In this tutorial, participants gained general information on the DBpedia knowledge Graph and the DBpedia community. The tutorial also provided information on the complete DBpedia knowledge Graph lifecycle, i.e. from extraction and modelling to publishing and maintenance of the DBpedia KG. A particular focus was put on the DBpedia Infrastructure, i.e. the DBpedia's databus publishing platform and the associated DBpedia services, i.e. DBpedia Spotlight, DBpedia Lookup, the DBpedia service endpoints and DBpedia Archivo. © 2021 Copyright for this paper by its authors

关键词： Open data

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Multilingual knowledge Systems as Linguistic Linked Open data for European language Grid

Multilingual Knowledge Systems as Linguistic Linked Open Dat...

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2021 workshops and tutorials - language data and knowledge, ldk 2021

作者： Vasilevich, Alena Wetzel, Michael Coreon GmbH Rungestrasse 20 Berlin10179 Germany

Creation and re-usability of language resources in accordance with Linked data principles is a valuable asset in the modern data world. In this paper we describe the contributions made to extend the LLOD stack with a new resource, Coreon MKS, bringing together concept-oriented, language-agnostic terminology management and graph-based knowledge organization. We dwell on our approach to mirroring of Coreon's original data structure to RDF and supplying it with a real-time SPARQL endpoint. We integrate MKS into the existing ELG infrastructure, using it as a platform for making the published MKS discoverable and retrievable via a industry-standard interface. While we apply this approach to LLOD-ify Coreon MKS, it can provide a relevant input for standardisation bodies and interoperability communities, acting as a blueprint for similar integration activities. © 2021 Copyright for this paper by its authors

关键词： Terminology

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Aspect-based sentiment analysis of conference review forms with LD-enabled review criteria

Aspect-based sentiment analysis of conference review forms w...

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2021 workshops and tutorials - language data and knowledge, ldk 2021

作者： Juranková, Sára Svátek, Vojtěch Ghidini, Chiara Prague University of Economics and Business Czech Republic Fondazione Bruno Kessler Trento Italy

Conference (or journal) review forms represent a particular kind of sentiment-bearing documents, which has been to date left largely unexplored. We set up a review corpus including a subset or a whole of anonymized conference reviews from four conferences. Furthermore, by a thorough manual analysis of the review forms and guidelines of eleven conferences we identified a set of generic review criteria, which we used as aspects in aspect-based sentiment analysis (SA) of the review form texts. A sentiment lexicon was created specifically for the domain of conference paper reviews. The first step of the lexicon construction method was the manual definition of a two-level taxonomy. In the second phase, noun phrases and adjectives frequently appearing in the given portions of the review were automatically collected. The results of the lexicon-based SA method were compared with the numerical scores from the reviews. The precision of criterion identification was evaluated at 57.38% and the recall at 53.44%;the sentiment polarity was correct in over 75% of cases. This is an improvement on the result of a similar sentiment analysis carried out in a comparable study. © 2021 Copyright for this paper by its authors

关键词： Linked data

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Exploring the Sensory Spaces of English Perceptual Verbs in Natural language data

Exploring the Sensory Spaces of English Perceptual Verbs in ...

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2021 workshops and tutorials - language data and knowledge, ldk 2021

作者： Girju, Roxana Peng, David Department of Linguistics University of Illinois at Urbana-Champaign UrbanaIL United States The Beckman Institute for Advanced Science and Technology UrbanaIL United States Department of Economics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL United States

In this study, we explore how language captures the meaning of words, in particular meaning related to sensory experiences learned from statistical distributions across texts. We focus on the most frequent perception verbs of English analyzed from an an Agentive vs. Experiential distinction across the five basic sensory modalities: Visual (to look vs. to see), Auditory (to listen vs. to hear), Tactile (to touch vs. to feel), Olfactory (to smell), and Gustatory (to taste). In this study we report on a data-driven approach based on distributional-semantic word embeddings and clustering models to identify and uncover the descriptors' sensory spaces of the perception verbs. In the analysis, we identified differences and similarities of the generated descriptors based on qualitative and quantitative differences of the perceptual experience they denote. For instance, our results show that while the perceptual spaces of the experiential verbs like to see, hear show a more detached, logical way of knowing and learning, their agentive counterparts (to look, listen) provide a more intentional as well as more intimate and intuitive way of discovering and interacting with the world around us. We believe that such an approach has a high potential to expand our understanding and the applicability of such sensory spaces to different fields of social and cultural analysis. Research on the semantic organization of sensory spaces for various applications might benefit from an the Agentive/Experiential account to address the complexity of multiple senses wired with each other in still unexplored ways. © Roxana Girju and David Peng

关键词： Sensory perception

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LOD-connected offensive language ontology and tagset enrichment

LOD-connected offensive language ontology and tagset enrichm...

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2021 workshops and tutorials - language data and knowledge, ldk 2021

作者： Lewandowska-Tomaszczyk, Barbara Žitnik, Slavko Bączkowska, Anna Liebeskind, Chaya Mitrović, Jelena Oleskeviciene, Giedre Valunaite Department of Language and Communication State University of Applied Sciences Konin Poland University of Ljubljana Faculty for Computer and Information Science Večna pot 113 LjubljanaSI-1000 Slovenia Department of Glottodidactics and Natural Language Processing University of Gdansk Poland Department of Computer Science Jerusalem College of Technology Jerusalem Israel University of Passau Germany The Institute for Artificial Intelligence Research and Development of Serbia Serbia Institute of Humanities Mykolas Romeris University Ateities 20 VilniusLT-08303 Lithuania

The main focus of the paper is the definitional revision and enrichment of offensive language typology, making reference to publicly available offensive language datasets and testing them on available pre-trained lexical embedding systems. We review over 60 available corpora and compare tagging schemas applied there while making an attempt to explain semantic differences between particular concepts of the category OFFENSIVE in English. A finite set of classes that cover aspects of offensive language representation along with linguistically sound explanations is presented, based on the categories originally proposed by Zampieri et al. [1, 2] in terms of offensive language categorization schemata and tested by means of Sketch Engine tools on a large web-based corpus. The schemata are juxtaposed and discussed with reference to non-contextual word embeddings FastText, Word2Vec, and Glove. The methodology for mapping from existing corpora to a unified ontology as presented in this paper is provided. The proposed schema will enable further comparable research and effective use of corpora of languages other than English. It will also be applied in building an enriched tagset to be trained and used on new data, with the application of recently developed LLOD techniques [3]. © 2021 Copyright for this paper by its authors

关键词： Ontology

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Enhancing the Automatic Identification of Common Math Misconceptions Using Natural language Processing 24th

Enhancing the Automatic Identification of Common Math Miscon...

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24th International Conference on Artificial Intelligence in Education (AIED)

作者： Gorgun, Guher Botelho, Anthony F. Univ Alberta Edmonton AB T6G 2G5 Canada Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9783031363351;9783031363368

In order to facilitate student learning, it is important to identify and remediate misconceptions and incomplete knowledge pertaining to the assigned material. In the domain of mathematics, prior research with computer-based learning systems has utilized the commonality of incorrect answers to problems as a way of identifying potential misconceptions among students. Much of this research, however, has been limited to the use of close-ended questions, such as multiple-choice and fill-in-the-blank problems. In this study, we explore the potential usage of natural language processing and clustering methods to examine potential misconceptions across student answers to both close- and open-ended problems. We find that our proposed methods show promise for distinguishing misconception from non-conception, but may need further development to improve the interpretability of specific misunderstandings exhibited through student explanations.

关键词： misconceptions sentence-BERT intelligent tutoring system natural language processing

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