Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

@inproceedings{Knight2016ProceedingsOT,
  title={Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  author={Kevin Knight and Ani Nenkova and Owen Rambow},
  booktitle={NAACL 2016},
  year={2016}
}
De-identifying Spanish medical texts - named entity recognition applied to radiology reports
TLDR
The strategy proposed, combining named entity recognition tasks with randomization of entities, is suitable for Spanish radiology reports and does not require a big training corpus, thus it could be easily extended to other languages and medical texts, such as electronic health records. Expand
Efficient Domain-Specific News Push Service Using Deep Learning Based Text Regression without Users’ Information
TLDR
A deep learning model for important news push service, referred to as HMA, which consists of a Hierarchical attention network and a Multi-head Attention mechanism for non-linear text regression and shows that HMA outperforms other alternative deep learning models due to the hierarchical structure and multi-head attention mechanism. Expand
Factorization of Fact-Checks for Low Resource Indian Languages
TLDR
This paper introduces FactDRIL: the first large scale multilingual Fact-checking Dataset for Regional Indian Languages, an exhaustive dataset across 7 months covering 11 low-resource languages and presents the detailed characterization of three M’s (multi-lingual, multi-media,Multi-domain) in the FactDRil accompanied with the complete list of other varied attributes making it a unique dataset to study. Expand
Image Captioning using Deep Stacked LSTMs, Contextual Word Embeddings and Data Augmentation
TLDR
Evaluation on widely used metrics have shown that the proposed Inception-ResNet Convolutional Neural Network as encoder, Hierarchical Context based Word Embeddings for word representations and a Deep Stacked Long Short Term Memory network as decoder leads to considerable improvement in model performance. Expand
Sentence Semantic Similarity Model Using Convolutional Neural Networks
In Natural Language Processing, determining the semantic likeness between sentences is an important research area. For example, there exists many possible semantics for a word (polysemy), and theExpand
A Comprehensive Survey of Multilingual Neural Machine Translation
TLDR
An in-depth survey of existing literature on multilingual neural machine translation is presented, which categorizes various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Expand
A Survey of Multilingual Neural Machine Translation
TLDR
An in-depth survey of existing literature on multilingual neural machine translation (MNMT) is presented and various approaches are categorized based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues, and challenges. Expand
Assessing Demographic Bias in Named Entity Recognition
TLDR
This work assesses the bias in various Named Entity Recognition systems for English across different demographic groups with synthetically generated corpora to shed light on potential biases in automated KB generation due to systematic exclusion of named entities belonging to certain demographics. Expand
Hierarchical Label Embedding Networks for Financial Document Sentiment Analysis
TLDR
A hierarchical label embedding neural network model that adopts hierarchical network structure to capture the structural information of financial documents and is more effective than other advanced methods on the established dataset is proposed. Expand
Faster Shift-Reduce Constituent Parsing with a Non-Binary, Bottom-Up Strategy
TLDR
A novel non-binary shift-reduce algorithm for constituent parsing that straightforwardly creates non- binary branchings with just one Reduce transition, allowing its direct application to any language without the need of further resources such as percolation tables. Expand
...
1
2
3
4
5
...