419 papers with code • 2 benchmarks • 9 datasets
The named entity recognition (NER) involves identification of key information in the text and classification into a set of predefined categories. This includes standard entities in the text like Part of Speech (PoS) and entities like places, names etc...
We also present a detailed empirical analysis of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale.
State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing.
Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance.
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
The Bidirectional long short-term memory networks (BiLSTM) have been widely used as an encoder in models solving the named entity recognition (NER) task.
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches.
Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET.
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs.