Crowd-Sourcing A High-Quality Dataset for Metaphor Identification in Tweets

Metaphor is one of the most important elements of human communication, especially in informal settings such as social media. There have been a number of datasets created for metaphor identification, however, this task has proven difficult due to the nebulous nature of metaphoricity. In this paper, we present a crowd-sourcing approach for the creation of a dataset for metaphor identification, that is able to rapidly achieve large coverage over the different usages of metaphor in a given corpus while maintaining high accuracy. We validate this methodology by creating a set of 2,500 manually annotated tweets in English, for which we achieve inter-annotator agreement scores over 0.8, which is higher than other reported results that did not limit the task. This methodology is based on the use of an existing classifier for metaphor in order to assist in the identification and the selection of the examples for annotation, in a way that reduces the cognitive load for annotators and enables quick and accurate annotation. We selected a corpus of both general language tweets and political tweets relating to Brexit and we compare the resulting corpus on these two domains. As a result of this work, we have published the first dataset of tweets annotated for metaphors, which we believe will be invaluable for the development, training and evaluation of approaches for metaphor identification in tweets

A supervised approach to taxonomy extraction using word embeddings

Large collections of texts are commonly generated by large organizations and making sense of these collections of texts is a significant challenge. One method for handling this is to organize the concepts into a hierarchical structure such that similar concepts can be discovered and easily browsed. This approach was the subject of a recent evaluation campaign, TExEval, however the results of this task showed that none of the systems consistently outperformed a relatively simple baseline.In order to solve this issue, we propose a new method that uses supervised learning to combine multiple features with a support vector machine classifier including the baseline features. We show that this outperforms the baseline and thus provides a stronger method for identifying taxonomic relations than previous method

Charge Pumping in Carbon Nanotubes

We demonstrate charge pumping in semiconducting carbon nanotubes by a traveling potential wave. From the observation of pumping in the nanotube insulating state we deduce that transport occurs by packets of charge being carried along by the wave. By tuning the potential of a side gate, transport of either electron or hole packets can be realized. Prospects for the realization of nanotube based single-electron pumps are discussed

A Quantum Dot in the Kondo Regime Coupled to Superconductors

The Kondo effect and superconductivity are both prime examples of many-body phenomena. Here we report transport measurements on a carbon nanotube quantum dot coupled to superconducting leads that show a delicate interplay between both effects. We demonstrate that the superconductivity of the leads does not destroy the Kondo correlations on the quantum dot when the Kondo temperature, which varies for different single-electron states, exceeds the superconducting gap energy

An Architecture for Data and Knowledge Acquisition for the Semantic Web: the AGROVOC Use Case

We are surrounded by ever growing volumes of unstructured and weakly-structured information, and for a human being, domain expert or not, it is nearly impossible to read, understand and categorize such information in a fair amount of time. Moreover, different user categories have different expectations: final users need easy-to-use tools and services for specific tasks, knowledge engineers require robust tools for knowledge acquisition, knowledge categorization and semantic resources development, while semantic applications developers demand for flexible frameworks for fast and easy, standardized development of complex applications. This work represents an experience report on the use of the CODA framework for rapid prototyping and deployment of knowledge acquisition systems for RDF. The system integrates independent NLP tools and custom libraries complying with UIMA standards. For our experiment a document set has been processed to populate the AGROVOC thesaurus with two new relationships

Reviewing the problem of learning non-taxonomic relationships of ontologies from text

Learning Non-Taxonomic Relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. This article discusses the problem of Learning Non-Taxonomic Relationships of ontologies and proposes a generic process for approaching it. Some techniques representing the state of the art of this field are discussed along with their advantages and limitations. Finally, a framework for Learning Non- Taxonomic Relationships being developed by the authors is briefly discussed. This framework intends to be a customizable solution to reach good effectiveness in the process of extraction of non-taxonomic relationships according to the characteristics of the corpus.This work is supported by CNPq, CAPES and FAPEMA, research funding agencies of the Brazilian government

An Introduction to Ontologies and Ontology Engineering

In the last decades, the use of ontologies in information systems has become more and more popular in various fields, such as web technologies, database integration, multi agent systems, natural language processing, etc. Artificial intelligent researchers have initially borrowed the word “ontology” from Philosophy, then the word spread in many scientific domain and ontologies are now used in several developments. The main goal of this chapter is to answer generic questions about ontologies, such as: Which are the different kinds of ontologies? What is the purpose of the use of ontologies in an application? Which methods can I use to build an ontology

Evaluating the semantic web: a task-based approach

The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape