Papers with node2vec
Dense Node Representation for Geolocation (D19-55)
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| Challenge: | Existing methods for geolocation use sparse adjacency matrices of connections, which grow exponentially with the number of users. |
| Approach: | They propose two methods to learn continuous node representations from social media posts and textual user mentions. |
| Outcome: | The proposed methods improve performance over previous sparse graph representations. |
Can Network Embedding of Distributional Thesaurus Be Combined with Word Vectors for Better Representation? (N18-1)
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| Challenge: | Distributed representations of words learned from text have proved to be successful in various natural language processing tasks. |
| Approach: | They propose to embed a distributional thesaurus network into dense word vectors and compare them to state-of-the-art word representations. |
| Outcome: | The proposed representations improve performance against state-of-the-art word representations even without handcrafted lexical resources. |
Using Distributional Thesaurus Embedding for Co-hyponymy Detection (2020.lrec-1)
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| Challenge: | Existing methods to detect lexical relations among distributionally similar words have been proposed to solve this problem. |
| Approach: | They propose to use distributional semantic models to detect co-hyponymy relations by embedding them into the distributional thesaurus. |
| Outcome: | The proposed model outperforms the state-of-the-art models for binary classification of co-hyponymy vs. hypernymy, as well as co-meronymy by huge margins. |