Integrating Subject Field Codes into WordNet

By Bernardo Magnini, Gabriela Maria Chiara Cavaglia. International Conference on Language Resources and Evaluation, 2000

460 citations recorded by the source corpus.

In this paper, we present a lexical resource where WordNet synsets are annotated with Subject Field Codes. We discuss both the methodological issues we dealt with and the annotation techniques used. A quantitative analysis of the resource coverage, as well as a qualitative evaluation of the proposed annotations, are reported.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d4c3be2aacbdde5e3d9a6f6e21e4ea90ca6d6827

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In Artificial Intelligence

Citations in the corpus

Learning to Merge Word Senses (2007, methodology): “Additionally, we use the WordNet domain dataset described in (Magnini and Cavaglia, 2000; Ben-tivogli et al., 2004).”
Structural semantic interconnection: a knowledge-based approach to Word Sense Disambiguation (2005, methodology): “However, we preferred the lable data set described in [19] From this sentence, the following semantic cooccurrence is generated: (color#1 delay#1 wide- screen#1 early#1 fifties#1). b. LDC-DSO [21] is a corpus where each document is a collection of sentences having a certain word in common.”
Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance (2006, background): “domain labels produced semi-automatically by Magnini and Cavaglià (2000) for each WordNet synset (we exclude the general-purpose label, called FACTOTUM).”
Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization (2016, background): “Additionaly, a different level of granularity of the concepts, such as WordNet Domains (Magnini and Cavaglia, 2000) could be explored.”
Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity (2013, unknown): “The classifier also made use of resources such as topic signatures data (Agirre and de Lacalle, 2004), the WordNet domain dataset (Magnini and Cavaglià, 2000), and the mappings of WordNet senses to ODE senses produced by Navigli (2006).”
WordNet Affect: an Affective Extension of WordNet (2004, methodology): “WORDNET-AFFECT (like WORDNET DOMAINS) is freely available for research purposes.”
YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames (2016, background): “This yields, e.g. <wikicat_Norwegian_ballet_dancers> <wordnet_ballet_dancer_109834699> <wordnet_dancer_109989502> rdfs:subClassOf rdfs:subClassOf YAGO includes WordNet Domains [18], which groups words into 167 thematic domains, and allows, e.g., searching for entities related to computer science .”
Finding Predominant Word Senses in Untagged Text (2004, methodology): “Additionally, we evaluated our method quantitatively using the Subject Field Codes (SFC) resource (Magnini and Cavaglià, 2000) which annotates WordNet synsets with domain labels.”