Signals are emerging pieces of information relevant to a given context and offer potential for strategic advantage in a multitude of domains. However, sorting the signal from noise on large textual data is a very tedious process for humans. We introduce a scalable approach that extracts signals from hundreds of crawled sources and maps their metadata to a knowledge graph by exploiting state-of-the-art neural models for natural language understanding
Neural language models have drastically changed the landscape of natural language processing (NLP). ...
The rapid increase of information available on the Internet, much of it textual, calls for computati...
This paper addresses the problem of detecting and recognizing text in images acquired ‘in the wild’....
Signals are emerging pieces of information relevant to a given context and offer potential for strat...
Recent years have witnessed increasing interests in developing interpretable models in Natural Langu...
In intelligent analysis of large amounts of text, not any single clue indicates reliably that a patt...
The general goal of text simplification (TS) is to reduce text complexity for human consumption. In ...
Recent years have witnessed the emerging success of leveraging syntax graphs for the target sentimen...
Recent NLP literature has seen growing interest in improving model interpretability. Along this dire...
In intelligent analysis of large amounts of text, not any single clue indicates reliably that a patt...
https://aclanthology.org/2021.emnlp-main.50/International audienceThe impressive capabilities of rec...
Large pretrained language models using the transformer neural network architecture are becoming a do...
Current research state-of-the-art in automatic data-to-text generation, a major task in natural lang...
Text classification is a fundamental language task in Natural Language Processing. A variety of sequ...
The field of service automation is progressing rapidly, and increasingly complex tasks are being aut...
Neural language models have drastically changed the landscape of natural language processing (NLP). ...
The rapid increase of information available on the Internet, much of it textual, calls for computati...
This paper addresses the problem of detecting and recognizing text in images acquired ‘in the wild’....
Signals are emerging pieces of information relevant to a given context and offer potential for strat...
Recent years have witnessed increasing interests in developing interpretable models in Natural Langu...
In intelligent analysis of large amounts of text, not any single clue indicates reliably that a patt...
The general goal of text simplification (TS) is to reduce text complexity for human consumption. In ...
Recent years have witnessed the emerging success of leveraging syntax graphs for the target sentimen...
Recent NLP literature has seen growing interest in improving model interpretability. Along this dire...
In intelligent analysis of large amounts of text, not any single clue indicates reliably that a patt...
https://aclanthology.org/2021.emnlp-main.50/International audienceThe impressive capabilities of rec...
Large pretrained language models using the transformer neural network architecture are becoming a do...
Current research state-of-the-art in automatic data-to-text generation, a major task in natural lang...
Text classification is a fundamental language task in Natural Language Processing. A variety of sequ...
The field of service automation is progressing rapidly, and increasingly complex tasks are being aut...
Neural language models have drastically changed the landscape of natural language processing (NLP). ...
The rapid increase of information available on the Internet, much of it textual, calls for computati...
This paper addresses the problem of detecting and recognizing text in images acquired ‘in the wild’....
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