D.G. Maynard and S. Ananiadou. Term extraction using a similarity-based approach. In Recent Advances in Computational Terminology. John Benjamins, 1999. Home/Search Document Details and Download
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Hybrid Text Mining for Finding Abbreviations and Their Definitions - Park, Byrd (2001) (3 citations) (Correct)
....we noticed that technical documents contain a lot of abbreviated terms, which carry important knowledge about the domains. We concluded that the correct recognition of abbreviations and their definitions is very important for understanding the documents and for extracting information from them [1, 6, 9, 11]. An abbreviation is usually formed by a simple method: taking zero or more letters from each word of its definition. However, the tendency to make unique, interesting abbreviations is growing. So, it is easy to find new kinds of abbreviations which cannot be processed by hard coded ....
....domainspecific knowledge. Thus, the ability to find correct abbreviations and their definitions is very important to being able to utilize the information contained in those documents. It is also very useful for many NLP applications such as information retrieval [1] and glossary extraction [4, 9, 11]. The proposed method has the following advantages: 1) It is simple and fast. A small number of formation rules can describe many abbreviations. By keeping these rules in the rulebase, this system can process most abbreviations by simple pattern matches. Furthermore, the abbreviation matcher ....
Maynard, Diana and Sophia Anaiadou. Term Extraction using a Similarity-based Approach. In Recent Advances in Computational Terminology, John Benjamins, 1999.
D2.2.3: State of the art on ontology - Alignment Coordinator Jrme Self-citation (Maynard) (Correct)
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D.G. Maynard and S. Ananiadou. Term extraction using a similarity-based approach. In Recent Advances in Computational Terminology. John Benjamins, 1999.
TRUCKS: a model for automatic multi-word term recognition - Maynard, Ananiadou (2000) (1 citation) Self-citation (Maynard Ananiadou) (Correct)
....of three layers: 1. a base statistical layer, determined by the C Value [14] selects candidate terms from the text. 2. a middle layer, determined by a Context Weight [15] combines linguistic and statistical information about context words. 3. an upper layer, determined by an Importance Weight [24], considers syntactic, semantic and terminological information about context words. These three weights are combined to form the SNC Value, which is described more fully in the following 2 sections. INFORMATION MODULE RESULT Importance SNC Value NC Value C Value term context Context ....
D.G. Maynard and S. Ananiadou. Term extraction using a similarity-based approach. In Recent Advances in Computational Terminology. John Benjamins, 1999. to appear.
Modelling Syntactic Context in Automatic Term Extraction - Basili, Pazienza, Zanzotto (2001) (Correct)
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Diana Maynard and Sophia Ananiadou. Term extraction using a similarity-based approach. In Didier Bourigault, Christian Jacquemin, and Marie-Claude L'Homme, editors, Recent Advances in Computational Terminology, 2000.
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