The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
Wikipedia-based Semantic Interpretation for Natural Language Processing
Open accessAbstract
Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.
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Cited by
Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020) · Business Process Management Journal · doi:10.1108/bpmj-10-2019-0411 · Cited by 1,280
TAGME: on-the-fly annotation of short text fragments (by wikipedia entities)
Ferragina, P., & Scaiella, U. (2010) · Proceedings of the 19th ACM international conference on Information and knowledge management · doi:10.1145/1871437.1871689 · Cited by 743
Good debt or bad debt: Detecting semantic orientations in economic texts
Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23062 · Cited by 513
“…enrichment of lexicon with weights for di ff erent concepts and events, an important direction for future research will be to examine how phrase level models can be merged with content models (Gabrilovich & Markovitch, 2010; Malo, Sinha, Wallenius, & Korhonen, 2011; Malo, Si-itari, & Sinha, 2013)).”
BackgroundParticipation Is not a Design Fix for Machine Learning
Sloane, M., Moss, E., Awomolo, O., & Forlano, L. (2022) · Equity and Access in Algorithms, Mechanisms, and Optimization · doi:10.1145/3551624.3555285 · Cited by 267
Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task
König, A., Linz, N., Tröger, J., Wolters, M., Alexandersson, J., & Robert, P. (2018) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000487852 · Cited by 77
Metadata
- Authors
- Gabrilovich, E., & Markovitch, S.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2009-03-30
- Volume
- 34
- Pages
- 443-498
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2099938389
- Cited by
- 377
- References
- 101
- FWCI
- 31.81
- Type
- journal article
- Language
- en
- Open access
- diamond
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