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Found 16 papers, 7 papers with code
One-shot to Weakly-Supervised Relation Classification using Language Models
Unfortunately, both annotation methodologies are costly and time-consuming since they depend on intensive human labour for annotation or for knowledge base creation.
DoLFIn: Distributions over Latent Features for Interpretability
Interpreting the inner workings of neural models is a key step in ensuring the robustness and trustworthiness of the models, but work on neural network interpretability typically faces a trade-off: either the models are too constrained to be very useful, or the solutions found by the models are too complex to interpret.
Revisiting Unsupervised Relation Extraction
Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs).
Boosting Entity Linking Performance by Leveraging Unlabeled Documents
First, we construct a high recall list of candidate entities for each mention in an unlabeled document.
Ranked #17 on
Entity Disambiguation
on AIDA-CoNLL
Distant Learning for Entity Linking with Automatic Noise Detection
As the learning signal is weak and our surrogate labels are noisy, we introduce a noise detection component in our model: it lets the model detect and disregard examples which are likely to be noisy.
Improving Entity Linking by Modeling Latent Relations between Mentions
Entity linking involves aligning textual mentions of named entities to their corresponding entries in a knowledge base.
Optimizing Differentiable Relaxations of Coreference Evaluation Metrics
Coreference evaluation metrics are hard to optimize directly as they are non-differentiable functions, not easily decomposable into elementary decisions.
LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues
We introduce an LSTM-based method for dynamically integrating several word-prediction experts to obtain a conditional language model which can be good simultaneously at several subtasks.
Quantifying the vanishing gradient and long distance dependency problem in recursive neural networks and recursive LSTMs
Recursive neural networks (RNN) and their recently proposed extension recursive long short term memory networks (RLSTM) are models that compute representations for sentences, by recursively combining word embeddings according to an externally provided parse tree.
Unsupervised Dependency Parsing: Let's Use Supervised Parsers
We present a self-training approach to unsupervised dependency parsing that reuses existing supervised and unsupervised parsing algorithms.
Ranked #1 on
Unsupervised Dependency Parsing
on Penn Treebank
Compositional Distributional Semantics with Long Short Term Memory
We are proposing an extension of the recursive neural network that makes use of a variant of the long short-term memory architecture.
