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Haixun Wang

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18 papers

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TMLR Journal 2026 Journal Article

Bi-level Hierarchical Neural Contextual Bandits for Online Recommendation

Yunzhe Qi
Yao Zhou
Yikun Ban
Allan Stewart
Chuanwei Ruan
Jiachuan He
Shishir Kumar Prasad
Haixun Wang

Contextual bandit algorithms aim to identify the optimal choice among a set of candidate arms, based on their contextual information. Among others, neural contextual bandit algorithms have demonstrated generally superior performance compared to conventional linear and kernel-based methods. Nevertheless, neural methods can be inherently unsuitable for handling a large number of candidate arms due to their high computational cost when performing principled exploration. Motivated by the widespread availability of arm category information (e.g., movie genres, retailer types), we formulate contextual bandits as a bi-level online recommendation problem, and propose a novel neural bandit framework, named $\text{H}_{2}\text{N-Bandit}$, which utilizes a bi-level hierarchical neural architecture to mitigate the substantial computational cost found in conventional neural bandit methods. To demonstrate its theoretical effectiveness, we provide regret analysis under general over-parameterization settings, along with a guarantee for category-level recommendation. To illustrate its effectiveness and efficiency, we conduct extensive experiments on multiple real-world data sets, highlighting that $\text{H}_{2}\text{N-Bandit}$ can significantly reduce the computational cost over existing strong non-linear baselines, while achieving better or comparable performance under online recommendation settings.

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NeurIPS Conference 2023 Conference Paper

Graph-Structured Gaussian Processes for Transferable Graph Learning

Jun Wu
Lisa Ainsworth
Andrew Leakey
Haixun Wang
Jingrui He

Transferable graph learning involves knowledge transferability from a source graph to a relevant target graph. The major challenge of transferable graph learning is the distribution shift between source and target graphs induced by individual node attributes and complex graph structures. To solve this problem, in this paper, we propose a generic graph-structured Gaussian process framework (GraphGP) for adaptively transferring knowledge across graphs with either homophily or heterophily assumptions. Specifically, GraphGP is derived from a novel graph structure-aware neural network in the limit on the layer width. The generalization analysis of GraphGP explicitly investigates the connection between knowledge transferability and graph domain similarity. Extensive experiments on several transferable graph learning benchmarks demonstrate the efficacy of GraphGP over state-of-the-art Gaussian process baselines.

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IJCAI Conference 2017 Conference Paper

Entity Suggestion with Conceptual Expanation

Yi Zhang
Yanghua Xiao
Seung-won Hwang
Haixun Wang
X. Sean Wang
Wei Wang

Entity Suggestion with Conceptual Explanation (ESC) refers to a type of entity acquisition query in which a user provides a set of example entities as the query and obtains in return not only some related entities but also concepts which can best explain the query and the result. ESC is useful in many applications such as related-entity recommendation and query expansion. Many example based entity suggestion solutions are available in existing literatures. However, they are generally not aware of the concepts of query entities thus cannot be used for conceptual explanation. In this paper, we propose two probabilistic entity suggestion models and their computation solutions. Our models and solutions fully take advantage of the large scale taxonomies which consist of isA relations between entities and concepts. With our models and solutions, we can not only find the best entities to suggest but also derive the best concepts to explain the suggestion. Extensive evaluations on real data sets justify the accuracy of our models and the efficiency of our solutions.

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AAAI Conference 2017 Conference Paper

Graph-Based Wrong IsA Relation Detection in a Large-Scale Lexical Taxonomy

Jiaqing Liang
Yanghua Xiao
Yi Zhang
Seung-won Hwang
Haixun Wang

Knowledge base(KB) plays an important role in artiﬁcial intelligence. Much effort has been taken to both manually and automatically construct web-scale knowledge bases. Comparing with manually constructed KBs, automatically constructed KB is broader but with more noises. In this paper, we study the problem of improving the quality for automatically constructed web-scale knowledge bases, in particular, lexical taxonomies of isA relationships. We ﬁnd that these taxonomies usually contain cycles, which are often introduced by incorrect isA relations. Inspired by this observation, we introduce two kinds of models to detect incorrect isA relations from cycles. The ﬁrst one eliminates cycles by extracting directed acyclic graphs, and the other one eliminates cycles by grouping nodes into different levels. We implement our models on Probase, a state-of-the-art, automatically constructed, web-scale taxonomy. After processing tens of millions of relations, our models eliminate 74 thousand wrong relations with 91% accuracy.

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AAAI Conference 2017 Conference Paper

On the Transitivity of Hypernym-Hyponym Relations in Data-Driven Lexical Taxonomies

Jiaqing Liang
Yi Zhang
Yanghua Xiao
Haixun Wang
Wei Wang
Pinpin Zhu

Taxonomy is indispensable in understanding natural language. A variety of large scale, usage-based, data-driven lexical taxonomies have been constructed in recent years. Hypernym-hyponym relationship, which is considered as the backbone of lexical taxonomies can not only be used to categorize the data but also enables generalization. In particular, we focus on one of the most prominent properties of the hypernym-hyponym relationship, namely, transitivity, which has a signiﬁcant implication for many applications. We show that, unlike human crafted ontologies and taxonomies, transitivity does not always hold in data-driven lexical taxonomies. We introduce a supervised approach to detect whether transitivity holds for any given pair of hypernym-hyponym relationships. Besides solving the inferencing problem, we also use the transitivity to derive new hypernym-hyponym relationships for data-driven lexical taxonomies. We conduct extensive experiments to show the effectiveness of our approach.

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AAAI Conference 2016 Conference Paper

Fine-Grained Semantic Conceptualization of FrameNet

Jin-woo Park
Seung-won Hwang
Haixun Wang

Understanding verbs is essential for many natural language tasks. To this end, large-scale lexical resources such as FrameNet have been manually constructed to annotate the semantics of verbs (frames) and their arguments (frame elements or FEs) in example sentences. Our goal is to “semantically conceptualize” example sentences by connecting FEs to knowledge base (KB) concepts. For example, connecting Employer FE to company concept in the KB enables the understanding that any (unseen) company can also be FE examples. However, a naive adoption of existing KB conceptualization technique, focusing on scenarios of conceptualizing a few terms, cannot 1) scale to many FE instances (average of 29. 7 instances for all FEs) and 2) leverage interdependence between instances and concepts. We thus propose a scalable k-truss clustering and a Markov Random Field (MRF) model leveraging interdependence between conceptinstance, concept-concept, and instance-instance pairs. Our extensive analysis with real-life data validates that our approach improves not only the quality of the identiﬁed concepts for FrameNet, but also that of applications such as selectional preference.

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IJCAI Conference 2016 Conference Paper

Learning Defining Features for Categories

Bo Xu
Chenhao Xie
Yi Zhang
Yanghua Xiao
Haixun Wang
Wei Wang

Categories play a fundamental role in human cognition. Defining features (short for DFs) are the key elements to define a category, which enables machines to categorize objects. Categories enriched with their DFs significantly improve the machine's ability of categorization and benefit many applications built upon categorization. However, defining features can rarely be found for categories in current knowledge bases. Traditional efforts such as manual construction by domain experts are not practical to find defining features for millions of categories. In this paper, we make the first attempt to automatically find defining features for millions of categories in the real world. We formalize the defining feature learning problem and propose a bootstrapping solution to learn defining features from the features of entities belonging to a category. Experimental results show the effectiveness and efficiency of our method. Finally, we find defining features for overall 60, 247 categories with acceptable accuracy.

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AAAI Conference 2016 Conference Paper

Verb Pattern: A Probabilistic Semantic Representation on Verbs

Wanyun Cui
Xiyou Zhou
Hangyu Lin
Yanghua Xiao
Haixun Wang
Seung-won Hwang
Wei Wang

Verbs are important in semantic understanding of natural language. Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on verbs’ roles. These roles are too coarse to represent verbs’ semantics. In this paper, we introduce verb patterns to represent verbs’ semantics, such that each pattern corresponds to a single semantic of the verb. First we analyze the principles for verb patterns: generality and speciﬁcity. Then we propose a nonparametric model based on description length. Experimental results prove the high effectiveness of verb patterns. We further apply verb patterns to context-aware conceptualization, to show that verb patterns are helpful in semantic-related tasks.

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IJCAI Conference 2015 Conference Paper

Learning Term Embeddings for Hypernymy Identification

Zheng Yu
Haixun Wang
Xuemin Lin
Min Wang

Hypernymy identification aims at detecting if isA relationship holds between two words or phrases. Most previous methods are based on lexical patterns or the Distributional Inclusion Hypothesis, and the accuracy of such methods is not ideal. In this paper, we propose a simple yet effective supervision framework to identify hypernymy relations using distributed term representations (a. k. a term embeddings). First, we design a distance-margin neural network to learn term embeddings based on some pre-extracted hypernymy data. Then, we apply such embeddings as term features to identify positive hypernymy pairs through a supervision method. Experimental results demonstrate that our approach outperforms other supervised methods on two popular datasets and the learned term embeddings has better quality than existing term distributed representations with respect to hypernymy identification.

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IJCAI Conference 2015 Conference Paper

On Conceptual Labeling of a Bag of Words

Xiangyan Sun
Yanghua Xiao
Haixun Wang
Wei Wang

In natural language processing and information retrieval, the bag of words representation is used to implicitly represent the meaning of the text. Implicit semantics, however, are insufficient in supporting text or natural language based interfaces, which are adopted by an increasing number of applications. Indeed, in applications ranging from automatic ontology construction to question answering, explicit representation of semantics is starting to play a more prominent role. In this paper, we introduce the task of conceptual labeling (CL), which aims at generating a minimum set of conceptual labels that best summarize a bag of words. We draw the labels from a data driven semantic network that contains millions of highly connected concepts. The semantic network provides meaning to the concepts, and in turn, it provides meaning to the bag of words through the conceptual labels we generate. To achieve our goal, we use an information theoretic approach to trade-off the semantic coverage of a bag of words against the minimality of the output labels. Specifically, we use Minimum Description Length (MDL) as the criteria in selecting the best concepts. Our extensive experimental results demonstrate the effectiveness of our approach in representing the explicit semantics of a bag of words.

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IJCAI Conference 2015 Conference Paper

Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach

Yangqiu Song
Shusen Wang
Haixun Wang

Concepts embody the knowledge to facilitate our cognitive processes of learning. Mapping short texts to a large set of open domain concepts has gained many successful applications. In this paper, we unify the existing conceptualization methods from a Bayesian perspective, and discuss the three modeling approaches: descriptive, generative, and discriminative models. Motivated by the discussion of their advantages and shortcomings, we develop a generative + descriptive modeling approach. Our model considers term relatedness in the context, and will result in disambiguated conceptualization. We show the results of short text clustering using a news title data set and a Twitter message data set, and demonstrate the effectiveness of the developed approach compared with the state-of-the-art conceptualization and topic modeling approaches.

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IJCAI Conference 2015 Conference Paper

Query Understanding through Knowledge-Based Conceptualization

Zhongyuan Wang
Kejun Zhao
Haixun Wang
Xiaofeng Meng
Ji-Rong Wen

The goal of query conceptualization is to map instances in a query to concepts defined in a certain ontology or knowledge base. Queries usually do not observe the syntax of a written language, nor do they contain enough signals for statistical inference. However, the available context, i. e. , the verbs related to the instances, the adjectives and attributes of the instances, do provide valuable clues to understand instances. In this paper, we first mine a variety of relations among terms from a large web corpus and map them to related concepts using a probabilistic knowledge base. Then, for a given query, we conceptualize terms in the query using a random walk based iterative algorithm. Finally, we examine our method on real data and compare it to representative previous methods. The experimental results show that our method achieves higher accuracy and efficiency in query conceptualization.

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IS Journal 2014 Journal Article

Semantic Multidimensional Scaling for Open-Domain Sentiment Analysis

Erik Cambria
Yangqiu Song
Haixun Wang
Newton Howard

The ability to understand natural language text is far from being emulated in machines. One of the main hurdles to overcome is that computers lack both the common and common-sense knowledge that humans normally acquire during the formative years of their lives. To really understand natural language, a machine should be able to comprehend this type of knowledge, rather than merely relying on the valence of keywords and word co-occurrence frequencies. In this article, the largest existing taxonomy of common knowledge is blended with a natural-language-based semantic network of common-sense knowledge. Multidimensional scaling is applied on the resulting knowledge base for open-domain opinion mining and sentiment analysis.

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IJCAI Conference 2013 Conference Paper

Context-Dependent Conceptualization

Dongwoo Kim
Haixun Wang
Alice Oh

Conceptualization seeks to map a short text (i. e. , a word or a phrase) to a set of concepts as a mechanism of understanding text. Most of prior research in conceptualization uses human-crafted knowledge bases that map instances to concepts. Such approaches to conceptualization have the limitation that the mappings are not context sensitive. To overcome this limitation, we propose a framework in which we harness the power of a probabilistic topic model which inherently captures the semantic relations between words. By combining latent Dirichlet allocation, a widely used topic model with Probase, a large-scale probabilistic knowledge base, we develop a corpus-based framework for context-dependent conceptualization. Through this simple but powerful framework, we improve conceptualization and enable a wide range of applications that rely on semantic understanding of short texts, including frame element prediction, word similarity in context, ad-query similarity, and query similarity.

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IS Journal 2013 Journal Article

Knowledge-Based Approaches to Concept-Level Sentiment Analysis

Erik Cambria
Bjorn Schuller
Bing Liu
Haixun Wang
Catherine Havasi

The guest editors introduce novel approaches to opinion mining and sentiment analysis that go beyond a mere word-level analysis of text and provide concept-level methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

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IS Journal 2013 Journal Article

Statistical Approaches to Concept-Level Sentiment Analysis

Erik Cambria
Bjorn Schuller
Bing Liu
Haixun Wang
Catherine Havasi

The guest editors introduce novel statistical approaches to concept-level sentiment analysis that go beyond a mere syntactic-driven analysis of text and provide semantic-based methods. Such approaches allow a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.

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IJCAI Conference 2011 Conference Paper

Short Text Conceptualization Using a Probabilistic Knowledgebase

Yangqiu Song
Haixun Wang
Zhongyuan Wang
Hongsong Li
Weizhu Chen

Most of the text mining tasks, such as clustering, is dominated by statistical approaches that treat text as a bag of words. Semantics in the text is largely ignored in the mining process, and the mining results are often not easily interpretable. One particular challenge faced by such approaches is short text understanding, as short text lacks enough content from which a statistical conclusion can be drawn. For example, traditional topic analysis methods consider topic segments with tens of hundreds of words. Latent topic modeling, such as latent Dirichlet allocation, also requires sufficient words to infer document topic distribution. We enhance machine learning algorithms by first giving the machine a probabilistic knowledgebase that contains as big, rich, and consistent concepts (of worldly facts) as those in our mental world. Then a Bayesian inference mechanism is developed to conceptualize words and short text. We conducted comprehensive tests of our method on conceptualizing set of text terms, as well as clustering Twitter messages (tweets), which are typically approximately ten words long. Compared to latent semantic topic modeling and other four kinds of methods that using WordNet, Freebase and Wikipedia (category links and explicit semantic analysis), we show significant improvements in terms of tweets clustering accuracy.

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IJCAI Conference 2003 Conference Paper

Inductive Learning in Less Than One Sequential Data Scan

Wei Fan
Haixun Wang
Philip S. Yu
Shaw-Hwa Lo

Most recent research of scalable inductive learning on very large dataset, decision tree construction in particular, focuses on eliminating memory constraints and reducing the number of sequential data scans. However, state-of-the-art decision tree construction algorithms still require multiple scans over the data set and use sophisticated control mechanisms and data structures. We first discuss a general inductive learning framework that scans the dataset exactly once. Then, we propose an extension based on Hoeffding's inequality that scans the dataset less than once. Our frameworks are applicable to a wide range of inductive learners.

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