Microsoft Technology Licensing, LLC

Junyan Chen

Ruihua Song

Zheng Chen

Yi-Min Wang

Ji-Rong Wen

Weizhu Chen

Feng-Hsiung Hsu

Rui Gao

Pengcheng He

Qi Yao

Matthew Brigham Hall

Xiong-Fei Cai

Ning-Yi Xu

Yu Zhao

Yuting Sun

Katherine Winant Osborne

Zhicheng Dou

A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Interactive dialog training and communication system using artificial intelligence

Computer-readable media, computer systems, and computing devices facilitate enhancing a web index with uniform resource locator (URL)/non-encoding character (NEC) word pairs to facilitate relevance ranking of search results provided in response to a search query that includes NEC words. URLs are received from web pages and substrings extracted therefrom. Additional elements are received from the web page, word-broken into sequences of NEC words, and the NEC words are converted into encoding-language representations which are matched against the URL substrings to identify candidate URL/NEC pairs for utilization in relevance ranking.

Enhancing search-result relevance ranking using uniform resource locators for queries containing non-encoding characters

This document describes tools for adjusting anchor text weight to provide more relevant search engine results. Specifically, these tools take advantage of a site-relationship model to consider relationships not only between an anchor text source site and a destination page but also relationships between multiple anchor text source sites to improve web searches. Consideration of these relationships aids in determining a new an anchor text weight, which in turn results in more relevant search results.

Using anchor text with hyperlink structures for web searches

A method using a RankBoost-based algorithm to automatically select features for further ranking model training is provided. The method reiteratively applies a set of ranking candidates to a training data set comprising a plurality of ranking objects having a known pairwise ranking order. Each round of iteration applies a weight distribution of ranking object pairs, yields a ranking result by each ranking candidate, identifies a favored ranking candidate for the round based on the ranking results, and updates the weight distribution to be used in next iteration round by increasing weights of ranking object pairs that are poorly ranked by the favored ranking candidate. The method then infers a target feature set from the favored ranking candidates identified in the iterations.

Automated feature selection based on rankboost for ranking

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Beijing, China

Junyan Chen