A modular real-time clustering pipeline detects events from Twitter streams of millions of entities per minute and introduces new clustering quality metrics evaluated on a Firehose-derived dataset.
Named Entity Sequence Classification
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abstract
Named Entity Recognition (NER) aims at locating and classifying named entities in text. In some use cases of NER, including cases where detected named entities are used in creating content recommendations, it is crucial to have a reliable confidence level for the detected named entities. In this work we study the problem of finding confidence levels for detected named entities. We refer to this problem as Named Entity Sequence Classification (NESC). We frame NESC as a binary classification problem and we use NER as well as recurrent neural networks to find the probability of candidate named entity is a real named entity. We apply this approach to Tweet texts and we show how we could find named entities with high confidence levels from Tweets.
fields
cs.SI 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Real-time Event Detection on Social Data Streams
A modular real-time clustering pipeline detects events from Twitter streams of millions of entities per minute and introduces new clustering quality metrics evaluated on a Firehose-derived dataset.