Tutorial link, paper link

Abstract

Search engines deal with rich natural language data such as user queries and documents. Improving search quality requires processing and understanding such information effectively and efficiently, where natural language processing technologies are generally leveraged. As the representative data format in search systems, query or document data are represented as a sequence of words. Understanding such sequential information is generally a nontrivial task with traditional methods, with challenges from both data sparsity and data generalization. Deep learning models provide an opportunity to effectively extract the representative relevant information, thus better understand complicated semantics and underlying searcher intention. Recent years have seen the significant improvements brought by deep learning in various natural language processing tasks, indicating its great potential in promoting search systems.

However, developing deep learning models for natural language processing in search systems inevitably needs to meet the requirements of the complicated ecosystem of search engine. For example, some systems need frequent model updates, which excludes lengthy model training. In addition, low serving latency constraint precludes complex models from being used. How to keep model quality with relatively low complexity is a constant challenge faced by deep learning practitioners.

In this tutorial, we summarize the current effort of deep learning for natural language processing in search systems, as shown in Section 2. We first give an overview of search systems and natural language processing in search, followed by basic concepts of deep learning for natural language processing [8, 12, 13, 22]. Then we introduce how to apply deep natural language processing in search systems in practice: (a) query/document understanding that focuses on extracting and inferring relevant information from text data, specifically, classification [11, 16], entity tagging [10] and entity disambiguation [20]; (b) retrieval and ranking for semantically matching relevant documents [9, 14], where the strong latency restrictions can be alleviated by various methods [5, 7]; (c) language generation techniques designed to proactively guide/interact with users to further resolve ambiguity in the original search. Three representative generation tasks [3, 6, 15] are introduced that heavily rely on neural language modeling [1], sequence-to-sequence [21], or generative adversarial networks [4], etc. At last, we share our hands-on experience with LinkedIn search in real-world scenarios.

This tutorial gives a comprehensive overview of applying deep natural language processing techniques in above components through an end-to-end search system. In addition to traditional search engine, we include several use cases of advanced search systems such as conversational search [16–19] and task oriented chatbots [23]. We also highlight several important future trends, such as interacting with users via query generation, and latency reduction to meet the industry standard [2].

Tutors

Weiwei Guo is a senior software engineer at LinkedIn where he leads several efforts to apply the deep learning models into search productions. Previously, he was a research scientist in Yahoo! Labs working on query understanding. He obtained his Ph.D. in Computer Science from Columbia University in 2015, and B.S from Sun Yat-sen University. Weiwei has published over 20 papers in top conferences including ACL, EMNLP, NAACL, SIGIR with 1000+ citations. He has served Program Committee for many conferences including ACL, EMNLP, NAACL, AAAI.

Huiji Gao leads the AI Algorithms Foundation team at LinkedIn. He has broad interests in machine learning/AI and its applications, including recommender systems, computational advertising, search ranking, and natural language processing. He received Ph.D. in Computer Science from Arizona State University, and B.S./M.S. from Beijing University of Posts and Telecommunications. He has filed over 10 U.S. patents and published 40 publications in top journals and conferences including KDD, AAAI, CIKM, WWW, ICDM, SDM, DMKD with thousands of citations.

Jun Shi is a staff software engineer at LinkedIn, where he leads various efforts on promoting natural language processing in search with deep learning technologies. His research interest lies in the area of machine learning with emphasis on natural language processing. He was an author of CaffeOnSpark and TensorflowOnSpark. He was a contributor to Tensorflow and created verbs interface for Tensorflow. Jun Shi received a doctoral degree in Electrical Engineering from UCLA. He was a co-recipient of 2009 IEEE Communications Society & Information Theory Society Joint Paper Award.

Bo Long leads LinkedIn's AI Foundations team. He also worked at Particle Media, Yahoo! Labs, IBM Watson and Google Lab. He has 15 years of experience in data mining and machine learning with applications to web search, recommendation, and social network analysis. He holds dozens of innovations and has published peer-reviewed papers in top conferences and journals including ICML, KDD, ICDM, AAAI, SDM, CIKM, and KAIS. He has served as reviewers, workshops co-organizers, conference organizer committee members, and area chairs for multiple conferences, including KDD, NIPS, SIGIR, ICML, SDM, CIKM, JSM etc.

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