Papers by Tsuneaki Kato
Bridging the Defined and the Defining: Exploiting Implicit Lexical Semantic Relations in Definition Modeling (D19-1)
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| Challenge: | Existing definition modeling methods do not utilize lexical semantic relations between defined words and defining words. |
| Approach: | They propose definition modeling methods that use lexical semantic relations . they use unsupervised pattern-based word-pair embeddings that represent semantic relations of word pairs . |
| Outcome: | The proposed methods improve definition generation and learning embeddings from definitions. |
Undersampling Improves Hypernymy Prototypicality Learning (L18-1)
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| Challenge: | supervised hypernymy detection suffers from overfitting hypernies in training data. |
| Approach: | They propose a method that can alleviate the problem of overfitting hypernyms in training data by using distributional representations for unknown word pairs. |
| Outcome: | The proposed method alleviates the problem of overfitting hypernyms in training data and improves distributional prototypicality learning for unknown word pairs. |
Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations (N18-1)
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| Challenge: | Existing approaches to recognize lexical semantic relations between word pairs require that word pairs co-occur in a sentence. |
| Approach: | They propose to exploit lexico-syntactic paths between two target words to exploit the semantic relations between word pairs. |
| Outcome: | The proposed model can generalize the co-occurrences of word pairs and dependency paths and extract features capturing relational information from word pairs. |
Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space (D18-1)
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| Challenge: | Existing approaches to capture semantic relations of words in vector space are lacking information on lexico-syntactic patterns that connect word pairs in a corpus. |
| Approach: | They propose a pattern-based approach that exploits lexico-syntactic patterns as word pairs . they propose NLRA to generalize co-occurrences of word pairs and lexicon-sensitized embeddings of the word pairs that do not co-occur. |
| Outcome: | The proposed model outperforms existing models on measuring relational similarity . it can generalize word pairs and lexico-syntactic patterns and obtain embeddings of word pairs that do not co-occur . |