Search Results for author: Royal Sequiera

Found 4 papers, 0 papers with code

Integrating Lexical and Temporal Signals in Neural Ranking Models for Searching Social Media Streams

no code implementations25 Jul 2017 Jinfeng Rao, Hua He, Haotian Zhang, Ferhan Ture, Royal Sequiera, Salman Mohammed, Jimmy Lin

To our knowledge, we are the first to integrate lexical and temporal signals in an end-to-end neural network architecture, in which existing neural ranking models are used to generate query-document similarity vectors that feed into a bidirectional LSTM layer for temporal modeling.

Density Estimation Document Ranking

Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering

no code implementations25 Jul 2017 Royal Sequiera, Gaurav Baruah, Zhucheng Tu, Salman Mohammed, Jinfeng Rao, Haotian Zhang, Jimmy Lin

Most work on natural language question answering today focuses on answer selection: given a candidate list of sentences, determine which contains the answer.

Answer Selection

Estimating Code-Switching on Twitter with a Novel Generalized Word-Level Language Detection Technique

no code implementations ACL 2017 Shruti Rijhwani, Royal Sequiera, Monojit Choudhury, Kalika Bali, Ch Maddila, ra Shekhar

Word-level language detection is necessary for analyzing code-switched text, where multiple languages could be mixed within a sentence.

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