A survey on deep learning for named entity recognition
Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …
belonging to predefined semantic types such as person, location, organization etc. NER …
Named entity recognition and relation extraction: State-of-the-art
With the advent of Web 2.0, there exist many online platforms that result in massive textual-
data production. With ever-increasing textual data at hand, it is of immense importance to …
data production. With ever-increasing textual data at hand, it is of immense importance to …
Zero-shot transfer learning for event extraction
Most previous event extraction studies have relied heavily on features derived from
annotated event mentions, thus cannot be applied to new event types without annotation …
annotated event mentions, thus cannot be applied to new event types without annotation …
A review on entity relation extraction
Q Zhang, M Chen, L Liu - 2017 second international …, 2017 - ieeexplore.ieee.org
Because of large amounts of unstructured data generated on the Internet, entity relation
extraction is believed to have high commercial value. Entity relation extraction is a case of …
extraction is believed to have high commercial value. Entity relation extraction is a case of …
LearningToAdapt with word embeddings: Domain adaptation of Named Entity Recognition systems
Abstract The task of Named Entity Recognition (NER) is aimed at identifying named entities
in a given text and classifying them into pre-defined domain entity types such as persons …
in a given text and classifying them into pre-defined domain entity types such as persons …
A convolution-LSTM-based deep neural network for cross-domain MOOC forum post classification
Learners in a massive open online course often express feelings, exchange ideas and seek
help by posting questions in discussion forums. Due to the very high learner-to-instructor …
help by posting questions in discussion forums. Due to the very high learner-to-instructor …
Automatically detecting bystanders in photos to reduce privacy risks
Photographs taken in public places often contain bystanders-people who are not the main
subject of a photo. These photos, when shared online, can reach a large number of viewers …
subject of a photo. These photos, when shared online, can reach a large number of viewers …
Metaner: Named entity recognition with meta-learning
Recent neural architectures in named entity recognition (NER) have yielded state-of-the-art
performance on single domain data such as newswires. However, they still suffer from (i) …
performance on single domain data such as newswires. However, they still suffer from (i) …
Sequence labeling with meta-learning
Recent neural architectures in sequence labeling have yielded state-of-the-art performance
on single domain data such as newswires. However, they still suffer from (i) requiring …
on single domain data such as newswires. However, they still suffer from (i) requiring …
Effective use of bidirectional language modeling for transfer learning in biomedical named entity recognition
Biomedical named entity recognition (NER) is a fundamental task in text mining of medical
documents and has many applications. Deep learning based approaches to this task have …
documents and has many applications. Deep learning based approaches to this task have …