3,620 research outputs found
A User-Centered Concept Mining System for Query and Document Understanding at Tencent
Concepts embody the knowledge of the world and facilitate the cognitive
processes of human beings. Mining concepts from web documents and constructing
the corresponding taxonomy are core research problems in text understanding and
support many downstream tasks such as query analysis, knowledge base
construction, recommendation, and search. However, we argue that most prior
studies extract formal and overly general concepts from Wikipedia or static web
pages, which are not representing the user perspective. In this paper, we
describe our experience of implementing and deploying ConcepT in Tencent QQ
Browser. It discovers user-centered concepts at the right granularity
conforming to user interests, by mining a large amount of user queries and
interactive search click logs. The extracted concepts have the proper
granularity, are consistent with user language styles and are dynamically
updated. We further present our techniques to tag documents with user-centered
concepts and to construct a topic-concept-instance taxonomy, which has helped
to improve search as well as news feeds recommendation in Tencent QQ Browser.
We performed extensive offline evaluation to demonstrate that our approach
could extract concepts of higher quality compared to several other existing
methods. Our system has been deployed in Tencent QQ Browser. Results from
online A/B testing involving a large number of real users suggest that the
Impression Efficiency of feeds users increased by 6.01% after incorporating the
user-centered concepts into the recommendation framework of Tencent QQ Browser.Comment: Accepted by KDD 201
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an
essential role in semantic search. Prior conceptual graph construction
approaches typically extract high-frequent, coarse-grained, and time-invariant
concepts from formal texts. In real applications, however, it is necessary to
extract less-frequent, fine-grained, and time-varying conceptual knowledge and
build taxonomy in an evolving manner. In this paper, we introduce an approach
to implementing and deploying the conceptual graph at Alibaba. Specifically, We
propose a framework called AliCG which is capable of a) extracting fine-grained
concepts by a novel bootstrapping with alignment consensus approach, b) mining
long-tail concepts with a novel low-resource phrase mining approach, c)
updating the graph dynamically via a concept distribution estimation method
based on implicit and explicit user behaviors. We have deployed the framework
at Alibaba UC Browser. Extensive offline evaluation as well as online A/B
testing demonstrate the efficacy of our approach.Comment: Accepted by KDD 2021 (Applied Data Science Track
A Continuum-Based Approach for Tightness Analysis of Chinese Semantic Units
PACLIC 23 / City University of Hong Kong / 3-5 December 200
A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Machine Reading Comprehension (MRC) has become enormously popular recently
and has attracted a lot of attention. However, the existing reading
comprehension datasets are mostly in English. In this paper, we introduce a
Span-Extraction dataset for Chinese machine reading comprehension to add
language diversities in this area. The dataset is composed by near 20,000 real
questions annotated on Wikipedia paragraphs by human experts. We also annotated
a challenge set which contains the questions that need comprehensive
understanding and multi-sentence inference throughout the context. We present
several baseline systems as well as anonymous submissions for demonstrating the
difficulties in this dataset. With the release of the dataset, we hosted the
Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC
2018). We hope the release of the dataset could further accelerate the Chinese
machine reading comprehension research. Resources are available:
https://github.com/ymcui/cmrc2018Comment: 6 pages, accepted as a conference paper at EMNLP-IJCNLP 2019 (short
paper
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
Heterogeneous Metric Learning with Content-Based Regularization for Software Artifact Retrieval
The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been traditionally addressed through the textual query. In other words, information retrieval techniques will be exploited based on the textual similarity between queries and textual representation of software artifacts, which is generated by collecting words from comments, identifiers, and descriptions of programs. However, in addition to these semantic information, there are rich information embedded in source codes themselves. These source codes, if analyzed properly, can be a rich source for enhancing the efforts of software artifact retrieval. To this end, in this paper, we develop a feature extraction method on source codes. Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes. Moreover, we design a heterogeneous metric learning approach, which allows to integrate code features and text features into the same latent semantic space. This, in turn, can help to measure the artifact similarity by exploiting the joint power of both code and text features. Finally, extensive experiments on real-world data show that the proposed method can help to improve the performances of software artifact retrieval with a significant margin
Controlling Risk of Web Question Answering
Web question answering (QA) has become an indispensable component in modern
search systems, which can significantly improve users' search experience by
providing a direct answer to users' information need. This could be achieved by
applying machine reading comprehension (MRC) models over the retrieved passages
to extract answers with respect to the search query. With the development of
deep learning techniques, state-of-the-art MRC performances have been achieved
by recent deep methods. However, existing studies on MRC seldom address the
predictive uncertainty issue, i.e., how likely the prediction of an MRC model
is wrong, leading to uncontrollable risks in real-world Web QA applications. In
this work, we first conduct an in-depth investigation over the risk of Web QA.
We then introduce a novel risk control framework, which consists of a qualify
model for uncertainty estimation using the probe idea, and a decision model for
selectively output. For evaluation, we introduce risk-related metrics, rather
than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA.
The empirical results over both the real-world Web QA dataset and the academic
MRC benchmark collection demonstrate the effectiveness of our approach.Comment: 42nd International ACM SIGIR Conference on Research and Development
in Information Retrieva
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