14,641 research outputs found
Extracting semantic entities and events from sports tweets
Large volumes of user-generated content on practically every major issue and event are being created on the microblogging site Twitter. This content can be combined and processed to detect events, entities and popular moods to feed various knowledge-intensive practical applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. In this paper, we exploit various approaches to detect the named entities and significant micro-events from usersâ tweets during a live sports event. Here we describe how combining linguistic features with background knowledge and the use of Twitter-specific features can achieve high, precise detection results (f-measure = 87%) in different datasets. A study was conducted on tweets from cricket matches in the ICC World Cup in order to augment the event-related non-textual media with collective intelligence
Detecting Conflicts and Inconsistencies in Web Application Requirements
Web applications evolve fast. One of the main reasons for this
evolution is that new requirements emerge and change constantly. These new
requirements are posed either by customers or they are the consequence of
usersâ feedback about the application. One of the main problems when dealing
with new requirements is their consistency in relationship with the current
version of the application. In this paper we present an effective approach for
detecting and solving inconsistencies and conflicts in web software
requirements. We first characterize the kind of inconsistencies arising in web
applications requirements and then show how to isolate them using a modeldriven
approach. With a set of examples we illustrate our approach
Exploiting multimedia in creating and analysing multimedia Web archives
The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general
Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services
Presently, a very large number of public and private data sets are available
from local governments. In most cases, they are not semantically interoperable
and a huge human effort would be needed to create integrated ontologies and
knowledge base for smart city. Smart City ontology is not yet standardized, and
a lot of research work is needed to identify models that can easily support the
data reconciliation, the management of the complexity, to allow the data
reasoning. In this paper, a system for data ingestion and reconciliation of
smart cities related aspects as road graph, services available on the roads,
traffic sensors etc., is proposed. The system allows managing a big data volume
of data coming from a variety of sources considering both static and dynamic
data. These data are mapped to a smart-city ontology, called KM4City (Knowledge
Model for City), and stored into an RDF-Store where they are available for
applications via SPARQL queries to provide new services to the users via
specific applications of public administration and enterprises. The paper
presents the process adopted to produce the ontology and the big data
architecture for the knowledge base feeding on the basis of open and private
data, and the mechanisms adopted for the data verification, reconciliation and
validation. Some examples about the possible usage of the coherent big data
knowledge base produced are also offered and are accessible from the RDF-Store
and related services. The article also presented the work performed about
reconciliation algorithms and their comparative assessment and selection
Analysing Lexical Semantic Change with Contextualised Word Representations
This paper presents the first unsupervised approach to lexical semantic
change that makes use of contextualised word representations. We propose a
novel method that exploits the BERT neural language model to obtain
representations of word usages, clusters these representations into usage
types, and measures change along time with three proposed metrics. We create a
new evaluation dataset and show that the model representations and the detected
semantic shifts are positively correlated with human judgements. Our extensive
qualitative analysis demonstrates that our method captures a variety of
synchronic and diachronic linguistic phenomena. We expect our work to inspire
further research in this direction.Comment: To appear in Proceedings of the 58th Annual Meeting of the
Association for Computational Linguistics (ACL-2020
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