1 |
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Case-based Reasoning for Natural Language Queries over Knowledge Bases ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Improved Latent Tree Induction with Distant Supervision via Span Constraints ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Scaling Within Document Coreference to Long Texts
|
|
|
|
In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
|
|
BASE
|
|
Show details
|
|
11 |
Improved Latent Tree Induction with Distant Supervision via Span Constraints ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
The materials science procedural text corpus: Annotating materials synthesis procedures with shallow semantic structures
|
|
|
|
In: arXiv (2020)
|
|
BASE
|
|
Show details
|
|
13 |
Energy and Policy Considerations for Deep Learning in NLP ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL? ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Linguistically-Informed Self-Attention for Semantic Role Labeling ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Multilingual Relation Extraction using Compositional Universal Schema ...
|
|
|
|
Abstract:
Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns expressing relations from raw text. In most previous applications of universal schema, each textual pattern is represented as a single embedding, preventing generalization to unseen patterns. Recent work employs a neural network to capture patterns' compositional semantics, providing generalization to all possible input text. In response, this paper introduces significant further improvements to the coverage and flexibility of universal schema relation extraction: predictions for entities unseen in training and multilingual transfer learning to domains with no annotation. We evaluate our model through extensive experiments on the English and Spanish TAC KBP benchmark, outperforming the top system from TAC 2013 slot-filling using no handwritten patterns or additional annotation. We also consider a multilingual setting in which English training data entities ... : Accepted to NAACL 2016 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
|
|
URL: https://dx.doi.org/10.48550/arxiv.1511.06396 https://arxiv.org/abs/1511.06396
|
|
BASE
|
|
Hide details
|
|
19 |
Learning Dynamic Feature Selection for Fast Sequential Prediction ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Compositional Vector Space Models for Knowledge Base Completion ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|