An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification
| Autor: | Mihai Surdeanu, Marco Antonio Valenzuela-Escárcega, Robert Vacareanu, Rebecca Sharp |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Recurrent neural network Noun 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Artificial intelligence F1 score business computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | COLING |
| Popis: | This paper explores an unsupervised approach to learning a compositional representation function for multi-word expressions (MWEs), and evaluates it on the Tratz dataset, which associates two-word expressions with the semantic relation between the compound constituents (e.g. the label employer is associated with the noun compound government agency) (Tratz, 2011). The composition function is based on recurrent neural networks, and is trained using the Skip-Gram objective to predict the words in the context of MWEs. Thus our approach can naturally leverage large unlabeled text sources. Further, our method can make use of provided MWEs when available, but can also function as a completely unsupervised algorithm, using MWE boundaries predicted by a single, domain-agnostic part-of-speech pattern. With pre-defined MWE boundaries, our method outperforms the previous state-of-the-art performance on the coarse-grained evaluation of the Tratz dataset (Tratz, 2011), with an F1 score of 50.4%. The unsupervised version of our method approaches the performance of the supervised one, and even outperforms it in some configurations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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