Papers by Huda Hakami
Learning to Borrow– Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion (2022.naacl-main)
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| Challenge: | Existing methods to integrate text corpora with knowledge graphs (KGs) have been effective in various NLP tasks such as analyzing and predicting relationships between entities. |
| Approach: | They propose a method that borrows LDPs from entities that co-occur in sentences to represent entities that do not co-exist in a single sentence. |
| Outcome: | The proposed method improves the performance of prior methods such as TransE, DistMult, ComplEx and RotatE. |
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding (2021.eacl-main)
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| Challenge: | Existing methods for embedding entities and relations in knowledge graphs are heuristically motivated and theoretical understanding of such embeddables is underdeveloped. |
| Approach: | They extend the random walk model of word embeddings to Knowledge Graph Embeddings (KGEs) they propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
| Outcome: | The proposed learning objective is motivated by the theoretical analysis to learn KGEs from a given knowledge graph. |
Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection (C18-1)
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| Challenge: | Existing methods for representing semantic relations between words are unclear . identifying relations between word and entity is important for NLP applications . |
| Approach: | They propose to compute the vector offset between word embeddings to represent relation between two words . they show that PairDiff is an uncorrelated bilinear operator that can be simplified to a linear form . |
| Outcome: | The proposed method is surprisingly accurate and can be used on multiple word embeddings. |