Amazon Technologies, Inc.

A9.com, Inc.

Hyokun Yun

Santosh Rajagopalan

Yaser Al-Onaizan

Zornitsa Kozareva

Narayanan Sadagopan

Vishwanathan Swaminathan

Trishul Amit Madhukar Chilimbi

Sheng Zha

Sankalp Nayak

Choon Hui Teo

Iman Keivanloo

Devices and techniques are generally described for search using a micro-partitioned catalog. In some examples, a search system can determine a plurality of items in an online catalog. The search system can generate a similarity graph based on vector representations of the plurality of items and determine an item as a representative item for a cluster of items. The search system can receive a query and determine the cluster of items as a candidate cluster based on a comparison between the query and the representative item. The search system can determine a candidate item of the cluster of items based on a comparison between the query and the cluster of items.

Micro-partitioning based search

Techniques for named-entity recognition are described. An exemplary implementation of a method includes extracting character features for each word of the document using a first encoder; extracting word level representations of for each word position using a second encoder, the word level representations being a concatenation of spelling variants; classifying the word level representations according to a first decoder; and outputting the classifications as named-entity labels.

Neural models for named-entity recognition

A keyphrase extraction service implements techniques for determining a set of keyphrases associated with set of words. A word is selected from the set of words and a neural model is used to determine a label for the word based on features of the word and labels corresponding to other words of the set of words. The set of keyphrases is determined from the labels associated with the set of words.

Neural models for keyphrase extraction

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Redmond, WA, United States of America

Hyokun Yun