Search Results for author:
Found 10 papers, 6 papers with code
Relation Specific Transformations for Open World Knowledge Graph Completion
We propose an open-world knowledge graph completion model that can be combined with common closed-world approaches (such as ComplEx) and enhance them to exploit text-based representations for entities unseen in training.
ConTest: A Unit Test Completion Benchmark featuring Context
We introduce CONTEST, a benchmark for NLP-based unit test completion, the task of predicting a test’s assert statements given its setup and focal method, i. e. the method to be tested.
IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale
Our focus is on inductive link prediction models as a basis for practical tools that support knowledge engineers with exploring text collections and discovering and linking new (so-called open-world) entities to the knowledge graph.
Addressing Leakage in Self-Supervised Contextualized Code Retrieval
We address contextualized code retrieval, the search for code snippets helpful to fill gaps in a partial input program.
An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learning
We present a joint model for entity-level relation extraction from documents.
Ranked #4 on
Joint Entity and Relation Extraction
on DocRED
coreference-resolution
Document-level Relation Extraction
+2
ManyEnt: A Dataset for Few-shot Entity Typing
We introduce ManyEnt, a benchmark for entity typing models in few-shot scenarios.
Neural Entity Linking on Technical Service Tickets
Entity linking, the task of mapping textual mentions to known entities, has recently been tackled using contextualized neural networks.
Span-based Joint Entity and Relation Extraction with Transformer Pre-training
The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass.
Ranked #1 on
Joint Entity and Relation Extraction
on SciERC
(Cross Sentence metric)
Joint Entity and Relation Extraction
Named Entity Recognition (NER)
+1
Hamming Sentence Embeddings for Information Retrieval
In retrieval applications, binary hashes are known to offer significant improvements in terms of both memory and speed.
An Open-World Extension to Knowledge Graph Completion Models
We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i. e. to predict facts for entities unseen in training based on their textual description.
