Efficient Neural Query Auto Completion

Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to user experience. Generally it has two steps: generating completed query candidates according to query prefixes, and ranking them based on extracted features. Three major challenges are observed for a query auto completion system: (1) QAC has a strict online latency requirement. For each keystroke, results must be returned within tens of milliseconds, which poses a significant challenge in designing sophisticated language models for it. (2) For unseen queries, generated candidates are of poor quality as contextual information is not fully utilized. (3) Traditional QAC systems heavily rely on handcrafted features such as the query candidate frequency in search logs, lacking sufficient semantic understanding of the candidate. In this paper, we propose an efficient neural QAC system with effective context modeling to overcome these challenges. On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin. On the candidate ranking side, an unnormalized language model is proposed, which effectively captures deep semantics of queries. This approach presents better ranking performance over state-of-the-art neural ranking methods and reduces $\sim$95\% latency compared to neural language modeling methods. The empirical results on public datasets show that our model achieves a good balance between accuracy and efficiency. This system is served in LinkedIn job search with significant product impact observed.Comment: Accepted at CIKM 202

Negative Statements Considered Useful

Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities

A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion

Users may strive to formulate an adequate textual query for their information need. Search engines assist the users by presenting query suggestions. To preserve the original search intent, suggestions should be context-aware and account for the previous queries issued by the user. Achieving context awareness is challenging due to data sparsity. We present a probabilistic suggestion model that is able to account for sequences of previous queries of arbitrary lengths. Our novel hierarchical recurrent encoder-decoder architecture allows the model to be sensitive to the order of queries in the context while avoiding data sparsity. Additionally, our model can suggest for rare, or long-tail, queries. The produced suggestions are synthetic and are sampled one word at a time, using computationally cheap decoding techniques. This is in contrast to current synthetic suggestion models relying upon machine learning pipelines and hand-engineered feature sets. Results show that it outperforms existing context-aware approaches in a next query prediction setting. In addition to query suggestion, our model is general enough to be used in a variety of other applications.Comment: To appear in Conference of Information Knowledge and Management (CIKM) 201

Inferring User Needs and Tasks from User Interactions

The need for search often arises from a broad range of complex information needs or tasks (such as booking travel, buying a house, etc.) which lead to lengthy search processes characterised by distinct stages and goals. While existing search systems are adept at handling simple information needs, they offer limited support for tackling complex tasks. Accurate task representations could be useful in aptly placing users in the task-subtask space and enable systems to contextually target the user, provide them better query suggestions, personalization and recommendations and help in gauging satisfaction. The major focus of this thesis is to work towards task based information retrieval systems - search systems which are adept at understanding, identifying and extracting tasks as well as supporting user’s complex search task missions. This thesis focuses on two major themes: (i) developing efficient algorithms for understanding and extracting search tasks from log user and (ii) leveraging the extracted task information to better serve the user via different applications. Based on log analysis on a tera-byte scale data from a real-world search engine, detailed analysis is provided on user interactions with search engines. On the task extraction side, two bayesian non-parametric methods are proposed to extract subtasks from a complex task and to recursively extract hierarchies of tasks and subtasks. A novel coupled matrix-tensor factorization model is proposed that represents user based on their topical interests and task behaviours. Beyond personalization, the thesis demonstrates that task information provides better context to learn from and proposes a novel neural task context embedding architecture to learn query representations. Finally, the thesis examines implicit signals of user interactions and considers the problem of predicting user’s satisfaction when engaged in complex search tasks. A unified multi-view deep sequential model is proposed to make query and task level satisfaction prediction

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

Modélisation des comportements de recherche basé sur les interactions des utilisateurs

Les utilisateurs de systèmes d'information divisent normalement les tâches en une séquence de plusieurs étapes pour les résoudre. En particulier, les utilisateurs divisent les tâches de recherche en séquences de requêtes, en interagissant avec les systèmes de recherche pour mener à bien le processus de recherche d'informations. Les interactions des utilisateurs sont enregistrées dans des journaux de requêtes, ce qui permet de développer des modèles pour apprendre automatiquement les comportements de recherche à partir des interactions des utilisateurs avec les systèmes de recherche. Ces modèles sont à la base de multiples applications d'assistance aux utilisateurs qui aident les systèmes de recherche à être plus interactifs, faciles à utiliser, et cohérents. Par conséquent, nous proposons les contributions suivantes : un modèle neuronale pour apprendre à détecter les limites des tâches de recherche dans les journaux de requête ; une architecture de regroupement profond récurrent qui apprend simultanément les représentations de requête et regroupe les requêtes en tâches de recherche ; un modèle non supervisé et indépendant d'utilisateur pour l'identification des tâches de recherche prenant en charge les requêtes dans seize langues ; et un modèle de tâche de recherche multilingue, une approche non supervisée qui modélise simultanément l'intention de recherche de l'utilisateur et les tâches de recherche. Les modèles proposés améliorent les méthodes existantes de modélisation, en tenant compte de la confidentialité des utilisateurs, des réponses en temps réel et de l'accessibilité linguistique. Le respect de la vie privée de l'utilisateur est une préoccupation majeure, tandis que des réponses rapides sont essentielles pour les systèmes de recherche qui interagissent avec les utilisateurs en temps réel, en particulier dans la recherche par conversation. Dans le même temps, l'accessibilité linguistique est essentielle pour aider les utilisateurs du monde entier, qui interagissent avec les systèmes de recherche dans de nombreuses langues. Les contributions proposées peuvent bénéficier à de nombreuses applications d'assistance aux utilisateurs, en aidant ces derniers à mieux résoudre leurs tâches de recherche lorsqu'ils accèdent aux systèmes de recherche pour répondre à leurs besoins d'information.Users of information systems normally divide tasks in a sequence of multiple steps to solve them. In particular, users divide search tasks into sequences of queries, interacting with search systems to carry out the information seeking process. User interactions are registered on search query logs, enabling the development of models to automatically learn search patterns from the users' interactions with search systems. These models underpin multiple user assisting applications that help search systems to be more interactive, user-friendly, and coherent. User assisting applications include query suggestion, the ranking of search results based on tasks, query reformulation analysis, e-commerce applications, retrieval of advertisement, query-term prediction, mapping of queries to search tasks, and so on. Consequently, we propose the following contributions: a neural model for learning to detect search task boundaries in query logs; a recurrent deep clustering architecture that simultaneously learns query representations through self-training, and cluster queries into groups of search tasks; Multilingual Graph-Based Clustering, an unsupervised, user-agnostic model for search task identification supporting queries in sixteen languages; and Language-agnostic Search Task Model, an unsupervised approach that simultaneously models user search intent and search tasks. Proposed models improve on existing methods for modeling user interactions, taking into account user privacy, realtime response times, and language accessibility. User privacy is a major concern in Ethics for intelligent systems, while fast responses are critical for search systems interacting with users in realtime, particularly in conversational search. At the same time, language accessibility is essential to assist users worldwide, who interact with search systems in many languages. The proposed contributions can benefit many user assisting applications, helping users to better solve their search tasks when accessing search systems to fulfill their information needs

MINING ACTIONABLE INTENTS IN QUERY ENTITIES

Understanding search engine users’ intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users’ future actions. In this paper, we present a novel research for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, i.e. the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment based on the Action Mining (AM) query entity dataset from Actionable Knowledge Graph (AKG) task at NTCIR-13 suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users

Modeling concepts and their relationships for corpus-based query auto-completion

AbstractQuery auto-completion helps users to formulate their information needs by providing suggestion lists at every typed key. This task is commonly addressed by exploiting query logs and the approaches proposed in the literature fit well in web scale scenarios, where usually huge amounts of past user queries can be analyzed to provide reliable suggestions. However, when query logs are not available, e.g. in enterprise or desktop search engines, these methods are not applicable at all. To face these challenging scenarios, we present a novel corpus-based approach which exploits the textual content of an indexed document collection in order to dynamically generate query completions. Our method extracts informative text fragments from the corpus and it combines them using a probabilistic graphical model in order to capture the relationships between the extracted concepts. Using this approach, it is possible to automatically complete partial queries with significant suggestions related to the keywords already entered by the user without requiring the analysis of the past queries. We evaluate our system through a user study on two different real-world document collections. The experiments show that our method is able to provide meaningful completions outperforming the state-of-the art approach