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- 87 creator cedric-pruski.
- 87 creator chantal-reynaud.
- 87 creator duy-dinh.
- 87 creator julio-cesar-dos-reis.
- 87 creator marcos-da-silveira.
- 87 type InProceedings.
- 87 label "Identifying change patterns of concept attributes in ontology evolution".
- 87 sameAs 87.
- 87 abstract "Ontology versions are periodically released to ensure their usefulness and reliability over time. This potentially impacts dependent artefacts such as mappings and annotations. To deal with this issue re- quires to finely characterize ontology entities’ changes between ontology versions. This article proposes to identify change patterns at attribute values when an ontology evolves, to track textual statements describing concepts. We empirically evaluate our approach by using biomedical on- tologies, for which new ontology versions are frequently released. Our achieved results suggest the feasibility of the proposed techniques.".
- 87 hasAuthorList authorList.
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- 87 keyword "change patterns".
- 87 keyword "ontology changes".
- 87 keyword "ontology evolution".
- 87 keyword "ontology versioning".
- 87 keyword "semantic similarity".
- 87 title "Identifying change patterns of concept attributes in ontology evolution".
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- 93 creator harith-alani.
- 93 creator hassan-saif.
- 93 creator miriam-fernandez.
- 93 creator yulan-he.
- 93 type InProceedings.
- 93 label "SentiCircles for Contextual and Conceptual Semantic Sentiment analysis of Twitter".
- 93 sameAs 93.
- 93 abstract "Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words' sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure.".
- 93 hasAuthorList authorList.
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- 93 keyword "Semantics".
- 93 keyword "Sentiment analysis".
- 93 keyword "Twitter".
- 93 title "SentiCircles for Contextual and Conceptual Semantic Sentiment analysis of Twitter".
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- 10 creator lior-rokach.
- 10 creator nir-ofek.
- 10 type InProceedings.
- 10 label "Lechuzo: Weakly-Supervised System for Fine-Grained Sentiment Analysis".
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- 10 title "Lechuzo: Weakly-Supervised System for Fine-Grained Sentiment Analysis".
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