Search Results for author: Vivian S. Silva

Found 6 papers, 0 papers with code

XTE: Explainable Text Entailment

no code implementations25 Sep 2020 Vivian S. Silva, André Freitas, Siegfried Handschuh

Text entailment, the task of determining whether a piece of text logically follows from another piece of text, is a key component in NLP, providing input for many semantic applications such as question answering, text summarization, information extraction, and machine translation, among others.

Machine Translation Question Answering +2

On the Semantic Interpretability of Artificial Intelligence Models

no code implementations9 Jul 2019 Vivian S. Silva, André Freitas, Siegfried Handschuh

Artificial Intelligence models are becoming increasingly more powerful and accurate, supporting or even replacing humans' decision making.

Decision Making

Word Tagging with Foundational Ontology Classes: Extending the WordNet-DOLCE Mapping to Verbs

no code implementations20 Jun 2018 Vivian S. Silva, André Freitas, Siegfried Handschuh

Semantic annotation is fundamental to deal with large-scale lexical information, mapping the information to an enumerable set of categories over which rules and algorithms can be applied, and foundational ontology classes can be used as a formal set of categories for such tasks.

Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition

no code implementations LREC 2018 Vivian S. Silva, André Freitas, Siegfried Handschuh

Adopting a conceptual model composed of a set of semantic roles for dictionary definitions, we trained a classifier for automatically labeling definitions, preparing the data to be later converted to a graph representation.

World Knowledge

Semantic Relation Classification: Task Formalisation and Refinement

no code implementations WS 2016 Vivian S. Silva, Manuela Hürliman, Brian Davis, Siegfried Handschuh, André Freitas

This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora.

Classification General Classification +2

Categorization of Semantic Roles for Dictionary Definitions

no code implementations WS 2016 Vivian S. Silva, Siegfried Handschuh, André Freitas

Understanding the semantic relationships between terms is a fundamental task in natural language processing applications.

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