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Found 8 papers, 1 papers with code
Collaborative Storytelling with Large-scale Neural Language Models
We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far.
Multi-label Sound Event Retrieval Using a Deep Learning-based Siamese Structure with a Pairwise Presence Matrix
State of the art sound event retrieval models have focused on single-label audio recordings, with only one sound event occurring, rather than on multi-label audio recordings (i. e., multiple sound events occur in one recording).
Results of the WNUT2017 Shared Task on Novel and Emerging Entity Recognition
This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
Lexical Acquisition through Implicit Confirmations over Multiple Dialogues
We address the problem of acquiring the ontological categories of unknown terms through implicit confirmation in dialogues.
DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets
In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User-generated Text (WNUT) Shared Task {\#}2: Named Entity Recognition in Twitter.
Named Entity Recognition with Bidirectional LSTM-CNNs
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
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Named Entity Recognition
on Ontonotes v5 (English)
