Relation Classification: How Well Do Neural Network Approaches Work?
| Autor: | Harish Karnick, Renu Jain, Sri Nath Dwivedi |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Relation (database) Computer science business.industry Process (engineering) Machine learning computer.software_genre Class (biology) Task (project management) Set (abstract data type) Data set Range (mathematics) ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence business computer |
| Zdroj: | Knowledge Graphs and Semantic Web ISBN: 9783030653835 KGSWC |
| DOI: | 10.1007/978-3-030-65384-2_8 |
| Popis: | Relation classification is a well known task in NLP. It classifies relations that occur between two entities in sentences by assigning a label from a pre-defined set of abstract relation labels. A benchmark data set for this task is the SemEval-2010 Task 8 data set. Neural network approaches are currently the methods that give state-of-art results on a wide range of NLP problems. There is also the claim that the models trained on one task carry over to other tasks with only a small amount of fine tuning. Our experience suggests that for the relation classification problem while a wide variety of neural network methods work reasonably well it is very hard to improve performance significantly by including different kinds of syntactic and semantic information that intuitively should be important in signalling the relation label. We think that improved performance will be hard to achieve without injecting controlled class specific semantic information into the classification process. |
| Databáze: | OpenAIRE |
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