1 |
Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion ...
|
|
|
|
Abstract:
Prior work on integrating text corpora with knowledge graphs (KGs) to improve Knowledge Graph Embedding (KGE) have obtained good performance for entities that co-occur in sentences in text corpora. Such sentences (textual mentions of entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between two entities. However, it is not possible to represent relations between entities that do not co-occur in a single sentence using LDPs. In this paper, we propose and evaluate several methods to address this problem, where we borrow LDPs from the entity pairs that co-occur in sentences in the corpus (i.e. with mention entity pairs) to represent entity pairs that do not co-occur in any sentence in the corpus (i.e. without mention entity pairs). We propose a supervised borrowing method, SuperBorrow, that learns to score the suitability of an LDP to represent a without-mention entity pair using pre-trained entity embeddings and contextualised LDP representations. Experimental results show that SuperBorrow ... : Accepted in NAACL 2022 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://arxiv.org/abs/2204.13097 https://dx.doi.org/10.48550/arxiv.2204.13097
|
|
BASE
|
|
Hide details
|
|
2 |
Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings
|
|
|
|
BASE
|
|
Show details
|
|
4 |
I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Detect and Classify – Joint Span Detection and Classification for Health Outcomes ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Fine-Tuning Word Embeddings for Hierarchical Representation of Data Using a Corpus and a Knowledge Base for Various Machine Learning Applications
|
|
|
|
In: Comput Math Methods Med (2021)
|
|
BASE
|
|
Show details
|
|
8 |
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding.
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction.
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Learning to Compose Relational Embeddings in Knowledge Graphs
|
|
|
|
BASE
|
|
Show details
|
|
|
|