398 citations · 2.8k across the 98 of their papers we have counts for
6 papers · 2 filters
Syntax Helps ELMo Understand Semantics: Is Syntax Still Relevant in a Deep Neural Architecture for SRL?
Emma Strubell, Andrew McCallum
Do unsupervised methods for learning rich, contextualized token representations obviate the need for explicit modeling of linguistic structure in neural network models for semantic…
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension
Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan +2
We propose a neural machine-reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure…
Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking
Shikhar Murty*, Patrick Verga*, Luke Vilnis +2
Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchi…
Efficient Graph-based Word Sense Induction by Distributional Inclusion Vector Embeddings
Haw-Shiuan Chang, Amol Agrawal, Ananya Ganesh +4
Word sense induction (WSI), which addresses polysemy by unsupervised discovery of multiple word senses, resolves ambiguities for downstream NLP tasks and also makes word representa…
Linguistically-Informed Self-Attention for Semantic Role Labeling
Emma Strubell, Patrick Verga, Daniel Andor +2
Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dram…
Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction
Patrick Verga, Emma Strubell, Andrew McCallum
Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does no…