Neural text-to-SQL models, which translate natural language questions (NLQs) into SQL queries given a database schema, have achieved remarkable performance. However, database schemas frequently evolve to meet new requirements. Such schema evolution often leads to performance degradation for models trained on static schemas. Existing work either mainly focuses on simply paraphrasing some syntactic or semantic mappings among NLQ, DB and SQL, or lacks a comprehensive and controllable way to investigate the model robustness issue under the schema evolution, which is insufficient when facing the increasingly complex and rich database schema changes in reality, especially in the LLM era. To address the challenges posed by schema evolution, we present EvoSchema, a comprehensive benchmark designed to assess and enhance the robustness of text-to-SQL systems under real-world schema changes. EvoSchema introduces a novel schema evolution taxonomy, encompassing ten perturbation types across column-level and table-level modifications, systematically simulating the dynamic nature of database schemas. Through EvoSchema, we conduct an in-depth evaluation spanning different open-source and closed-source LLMs, revealing that table-level perturbations have a significantly greater impact on model performance compared to column-level changes. Furthermore, EvoSchema inspires the development of more resilient text-to-SQL systems, in terms of both model training and database design. The models trained on EvoSchema's diverse schema designs can force the model to distinguish the schema difference for the same questions to avoid learning spurious patterns, which demonstrate remarkable robustness compared to those trained on unperturbed data on average. This benchmark offers valuable insights into model behavior and a path forward for designing systems capable of thriving in dynamic, real-world environments.

Differentially private (DP) learning, notably DP stochastic gradient descent (DP-SGD), has limited applicability in fine-tuning gigantic pre-trained language models (LMs) for natural language processing tasks. The culprit is the perturbation of gradients (as gigantic as entire models), leading to significant efficiency and accuracy drops.

A wealth of human knowledge is expressed in structured tables, across web pages, scientific articles, spreadsheets, and databases. This wealth of knowledge is mirrored by diversity in the vast number of layout structures, content types, formats, and surface forms used to express tables. Recent advances in representation learning and knowledge representation have made progress in exploiting structural regularities in tabular data to unlock this knowledge. In this tutorial, we provide a survey of these advances for a host of table understanding tasks, including table segmentation, semantic typing of cells, transforming tables to knowledge graphs, entity linking, and table retrieval tasks for question answering.

Long story generation (LSG) is one of the coveted goals in natural language processing. Different from most text generation tasks, LSG requires to output a long story of rich content based on a much shorter text input, and often suffers from information sparsity. In this paper, we propose TopNet to alleviate this problem, by leveraging the recent advances in neural topic modeling to obtain high-quality skeleton words to complement the short input. In particular, instead of directly generating a story, we first learn to map the short text input to a low-dimensional topic distribution (which is pre-assigned by a topic model). Based on this latent topic distribution, we can use the reconstruction decoder of the topic model to sample a sequence of inter-related words as a skeleton for the story. Experiments on two benchmark datasets show that our proposed framework is highly effective in skeleton word selection and significantly outperforms the state-of-the-art models in both automatic evaluation and human evaluation.

Predicting pairwise relationships between nodes in graphs is a fundamental task in data mining with many real-world applications, such as link prediction on social networks, relation prediction on knowledge graphs, etc. A dominating methodology is to first use advanced graph representation methods to learn generic node representations and then build a pairwise prediction classifier with the target nodes' vectors concatenated as input. However, such methods suffer from low interpretability, as it is difficult to explain why certain relationships are predicted only based on their prediction scores. In this paper, we propose to model the pairwise interactions between neighboring nodes (i.e., contexts) of target pairs. The new formulation enables us to build more appropriate representations for node pairs and gain better model interpretability (by highlighting meaningful interactions). To this end, we introduce a unified framework with two general perspectives, node-centric and pair-centric, about how to model context pair interactions. We also propose a novel pair-centric context interaction model and a new pre-trained embedding, which represents the pair semantics and shows many attractive properties. We test our models on two common pairwise prediction tasks: link prediction task and relation prediction task, and compare them with graph feature-based, embedding-based, and Graph Neural Network (GNN)-based baselines. Our experimental results show the superior performance of the pre-trained pair embeddings and that the pair-centric interaction model outperforms all baselines by a large margin.

Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such tables, there has been tremendous progress on a variety of tasks in the area of table understanding. However, existing work generally relies on heavily-engineered task-specific features and model architectures. In this paper, we present TURL, a novel framework that introduces the pre-training/fine-tuning paradigm to relational Web tables. During pre-training, our framework learns deep contextualized representations on relational tables in an unsupervised manner. Its universal model design with pre-trained representations can be applied to a wide range of tasks with minimal task-specific fine-tuning. Specifically, we propose a structure-aware Transformer encoder to model the row-column structure of relational tables, and present a new Masked Entity Recovery (MER) objective for pre-training to capture the semantics and knowledge in large-scale unlabeled data. We systematically evaluate TURL with a benchmark consisting of 6 different tasks for table understanding (e.g., relation extraction, cell filling). We show that TURL generalizes well to all tasks and substantially outperforms existing methods in almost all instances.

Social influence analysis in microblogging services has attracted much attention in recent years. However, most previous studies were focused on measuring users' (topical) influence. Little effort has been made to discern and quantify how a user is influenced. Specifically, the fact that user i retweets a tweet from author j could be either because i is influenced by j (i.e., j is a topical authority), or simply because he is “influenced” by the content (interested in the content). To mine such influence patterns, we propose a novel Bayesian factorization model, dubbed Influence Beta-Poisson Factorization (IBPF). IBPF jointly factorizes the retweet data and tweet content to quantify latent topical factors of user preference, author influence and content influence. It generates every retweet record according to the sum of two causing terms: one representing author influence, and the other one derived from content influence. To control the impact of the two terms, for each user IBPF generates a probability for each latent topic by Beta distribution, indicating how strongly the user cares about the topical authority of the author. We develop an efficient variational inference algorithm for IBPF. We demonstrate the efficacy of IBPF on two public microblogging datasets.

In this paper, we study the problem of personalized product search under streaming scenarios. We address the problem by proposing a Dynamic Bayesian Metric Learning model, abbreviated as DBML, which can collaboratively track the evolutions of latent semantic representations of different categories of entities (i.e., users, products and words) over time in a joint metric space. In particular, unlike previous work using inner-product metric to model the affinities between entities, our DBML is a novel probabilistic metric learning approach that is able to avoid the contradicts, keep the triangle inequality in the latent space, and correctly utilize implicit feedbacks. For inferring dynamic embeddings of the entities, we propose a scalable online inference algorithm, which can jointly learn the latent representations of entities and smooth their changes across time, based on amortized inference. The inferred dynamic semantic representations of entities collaboratively inferred in a unified form by our DBML can benefit not only for improving personalized product search, but also for capturing the affinities between users, products and words. Experimental results on large datasets over a number of applications demonstrate that our DBML outperforms the state-of-the-art algorithms, and can effectively capture the evolutions of semantic representations of different categories of entities over time.

Unstructured clinical texts contain rich health-related information. To better utilize the knowledge buried in clinical texts, discovering synonyms for a medical query term has become an important task. Recent automatic synonym discovery methods leveraging raw text information have been developed. However, to preserve patient privacy and security, it is usually quite difficult to get access to large-scale raw clinical texts. In this paper, we study a new setting named synonym discovery on privacy-aware clinical data (i.e., medical terms extracted from the clinical texts and their aggregated co-occurrence counts, without raw clinical texts). To solve the problem, we propose a new framework SurfCon that leverages two important types of information in the privacy-aware clinical data, i.e., the surface form information, and the global context information for synonym discovery. In particular, the surface form module enables us to detect synonyms that look similar while the global context module plays a complementary role to discover synonyms that are semantically similar but in different surface forms, and both allow us to deal with the OOV query issue (i.e., when the query is not found in the given data). We conduct extensive experiments and case studies on publicly available privacy-aware clinical data, and show that SurfCon can outperform strong baseline methods by large margins under various settings.

This work studies product question answering (PQA) which aims to answer product-related questions based on customer reviews. Most recent PQA approaches adopt end2end semantic matching methodologies, which map questions and answers to a latent vector space to measure their relevance. Such methods often achieve superior performance but it tends to be difficult to interpret why. On the other hand, simple keyword-based search methods exhibit natural interpretability through matched keywords, but often suffer from the lexical gap problem. In this work, we develop a new PQA framework (named Riker) that enjoys the benefits of both interpretability and effectiveness. Riker mines rich keyword representations of a question with two major components, internal word re-weighting and external word association, which predict the importance of each question word and associate the question with outside relevant keywords respectively, and can be jointly trained under weak supervision with large-scale QA pairs. The keyword representations from Riker can be directly used as input to a keyword-based search module, enabling the whole process to be effective while preserving good interpretability. We conduct extensive experiments using Amazon QA and review datasets from 5 different departments, and our results show that Riker substantially outperforms previous state-of-the-art methods in both synthetic settings and real user evaluations. In addition, we compare keyword representations from Riker and those from attention mechanisms popularly used for deep neural networks through case studies, showing that the former are more effective and interpretable.

To accelerate software development, much research has been performed to help people understand and reuse the huge amount of available code resources. Two important tasks have been widely studied: code retrieval, which aims to retrieve code snippets relevant to a given natural language query from a code base, and code annotation, where the goal is to annotate a code snippet with a natural language description. Despite their advancement in recent years, the two tasks are mostly explored separately. In this work, we investigate a novel perspective of Code annotation for Code retrieval (hence called “CoaCor”), where a code annotation model is trained to generate a natural language annotation that can represent the semantic meaning of a given code snippet and can be leveraged by a code retrieval model to better distinguish relevant code snippets from others. To this end, we propose an effective framework based on reinforcement learning, which explicitly encourages the code annotation model to generate annotations that can be used for the retrieval task. Through extensive experiments, we show that code annotations generated by our framework are much more detailed and more useful for code retrieval, and they can further improve the performance of existing code retrieval models significantly.1

Stack Overflow (SO) has been a great source of natural language questions and their code solutions (i.e., question-code pairs), which are critical for many tasks including code retrieval and annotation. In most existing research, question-code pairs were collected heuristically and tend to have low quality. In this paper, we investigate a new problem of systematically mining question-code pairs from Stack Overflow (in contrast to heuristically collecting them). It is formulated as predicting whether or not a code snippet is a standalone solution to a question. We propose a novel Bi-View Hierarchical Neural Network which can capture both the programming content and the textual context of a code snippet (i.e., two views) to make a prediction. On two manually annotated datasets in Python and SQL domain, our framework substantially outperforms heuristic methods with at least 15% higher F1 and accuracy. Furthermore, we present StaQC (Stack Overflow Question-Code pairs), the largest dataset to date of ~148K Python and ~120K SQL question-code pairs, automatically mined from SO using our framework. Under various case studies, we demonstrate that StaQC can greatly help develop data-hungry models for associating natural language with programming language

Nowadays, increasingly more people are receiving medical diagnoses from healthcare-related question answering platforms as people can get diagnoses quickly and conveniently. However, such diagnoses from non-expert crowdsourcing users are noisy or even wrong due to the lack of medical domain knowledge, which can cause serious consequences. To unleash the power of crowdsourcing on healthcare question answering, it is important to identify trustworthy answers and filter out noisy ones from user-generated data. Truth discovery methods estimate user reliability degrees and infer trustworthy information simultaneously, and thus these methods can be adopted to discover trustworthy diagnoses from crowdsourced answers. However, existing truth discovery methods do not take into account the rich semantic meanings of the answers. In the light of this challenge, we propose a method to automatically capture the semantic meanings of answers, where answers are represented as real-valued vectors in the semantic space. To learn such vector representations from noisy user-generated data, we tightly combine the truth discovery and vector learning processes. In this way, the learned vector representations enable truth discovery method to model the semantic relations among answers, and the information trustworthiness inferred by truth discovery can help the procedure of vector representation learning. To demonstrate the effectiveness of the proposed method, we collect a large-scale real-world dataset that involves 219,527 medical diagnosis questions and 23,657 non-expert users. Experimental results show that the proposed method improves the accuracy of identified trustworthy answers due to the successful consideration of answers' semantic meanings. Further, we demonstrate the fast convergence and good scalability of the proposed method, which makes it practical for real-world applications.

Given a health-related question (such as "I have a bad stomach ache. What should I do?"), a medical self-diagnosis Android inquires further information from the user, diagnoses the disease, and ultimately recommend best solutions. One practical challenge to build such an Android is to ask correct questions and obtain most relevant information, in order to correctly pinpoint the most likely causes of health conditions. In this paper, we tackle this challenge, named "relevant symptom question generation": Given a limited set of patient described symptoms in the initial question (e.g., "stomach ache"), what are the most critical symptoms to further ask the patient, in order to correctly diagnose their potential problems? We propose an augmented long short-term memory (LSTM) framework, where the network architecture can naturally incorporate the inputs from embedding vectors of patient described symptoms and an initial disease hypothesis given by a predictive model. Then the proposed framework generates the most important symptom questions. The generation process essentially models the conditional probability to observe a new and undisclosed symptom, given a set of symptoms from a patient as well as an initial disease hypothesis. Experimental results show that the proposed model obtains improvements over alternative methods by over 30% (both precision and mean ordinal distance).

Collaborative networks are common in real life, where domain experts work together to solve tasks issued by customers. How to model the proficiency of experts is critical for us to understand and optimize collaborative networks. Traditional expertise models, such as topic model based methods, cannot capture two aspects of human expertise simultaneously: Specialization (what area an expert is good at?) and Proficiency Level (to what degree?). In this paper, we propose new models to overcome this problem. We embed all historical task data in a lower dimension space and learn vector representations of expertise based on both solved and unsolved tasks. Specifically, in our first model, we assume that each expert will only handle tasks whose difficulty level just matches his/her proficiency level, while experts in the second model accept tasks whose levels are equal to or lower than his/her proficiency level. Experiments on real world datasets show that both models outperform topic model based approaches and standard classifiers such as logistic regression and support vector machine in terms of prediction accuracy. The learnt vector representations can be used to compare expertise in a large organization and optimize expert allocation.

Named Entity Disambiguation is the task of disambiguating named entity mentions in natural language text and link them to their corresponding entries in a reference knowledge base (e.g. Wikipedia). Such disambiguation can help add semantics to plain text and distinguish homonymous entities. Previous research has tackled this problem by making use of two types of context-aware features derived from the reference knowledge base, namely, the context similarity and the semantic relatedness. Both features heavily rely on the cross-document hyperlinks within the knowledge base: the semantic relatedness feature is directly measured via those hyperlinks, while the context similarity feature implicitly makes use of those hyperlinks to expand entity candidates' descriptions and then compares them against the query context. Unfortunately, cross-document hyperlinks are rarely available in many closed domain knowledge bases and it is very expensive to manually add such links. Therefore few algorithms can work well on linkless knowledge bases. In this work, we propose the challenging Named Entity Disambiguation with Linkless Knowledge Bases (LNED) problem and tackle it by leveraging the useful disambiguation evidences scattered across the reference knowledge base. We propose a generative model to automatically mine such evidences out of noisy information. The mined evidences can mimic the role of the missing links and help boost the LNED performance. Experimental results show that our proposed method substantially improves the disambiguation accuracy over the baseline approaches.

Tables are pervasive on the Web. Informative web tables range across a large variety of topics, which can naturally serve as a significant resource to satisfy user information needs. Driven by such observations, in this paper, we investigate an important yet largely under-addressed problem: Given millions of tables, how to precisely retrieve table cells to answer a user question. This work proposes a novel table cell search framework to attack this problem. We first formulate the concept of a relational chain which connects two cells in a table and represents the semantic relation between them. With the help of search engine snippets, our framework generates a set of relational chains pointing to potentially correct answer cells. We further employ deep neural networks to conduct more fine-grained inference on which relational chains best match the input question and finally extract the corresponding answer cells. Based on millions of tables crawled from the Web, we evaluate our framework in the open-domain question answering (QA) setting, using both the well-known WebQuestions dataset and user queries mined from Bing search engine logs. On WebQuestions, our framework is comparable to state-of-the-art QA systems based on knowledge bases (KBs), while on Bing queries, it outperforms other systems with a 56.7% relative gain. Moreover, when combined with results from our framework, KB-based QA performance can obtain a relative improvement of 28.1% to 66.7%, demonstrating that web tables supply rich knowledge that might not exist or is difficult to be identified in existing KBs.

The big data era is witnessing a prevalent shift of data from homogeneous to heterogeneous, from isolated to linked. Exemplar outcomes of this shift are a wide range of graph data such as information, social, and knowledge graphs. The unique characteristics of graph data are challenging traditional search techniques like SQL and keyword search. Graph query is emerging as a promising complementary search form. In this paper, we study how to improve graph query by relevance feedback. Specifically, we focus on knowledge graph query, and formulate the graph relevance feedback (GRF) problem. We propose a general GRF framework that is able to (1) tune the original ranking function based on user feedback and (2) further enrich the query itself by mining new features from user feedback. As a consequence, a query-specific ranking function is generated, which is better aligned with the user search intent. Given a newly learned ranking function based on user feedback, we further investigate whether we shall re-rank the existing answers, or choose to search from scratch. We propose a strategy to train a binary classifier to predict which action will be more beneficial for a given query. The GRF framework is applied to searching DBpedia with graph queries derived from YAGO and Wikipedia. Experiment results show that GRF can improve the mean average precision by 80% to 100%.

Most recent question answering (QA) systems query large-scale knowledge bases (KBs) to answer a question, after parsing and transforming natural language questions to KBs-executable forms (e.g., logical forms). As a well-known fact, KBs are far from complete, so that information required to answer questions may not always exist in KBs. In this paper, we develop a new QA system that mines answers directly from the Web, and meanwhile employs KBs as a significant auxiliary to further boost the QA performance. Specifically, to the best of our knowledge, we make the first attempt to link answer candidates to entities in Freebase, during answer candidate generation. Several remarkable advantages follow: (1) Redundancy among answer candidates is automatically reduced. (2) The types of an answer candidate can be effortlessly determined by those of its corresponding entity in Freebase. (3) Capitalizing on the rich information about entities in Freebase, we can develop semantic features for each answer candidate after linking them to Freebase. Particularly, we construct answer-type related features with two novel probabilistic models, which directly evaluate the appropriateness of an answer candidate's types under a given question. Overall, such semantic features turn out to play significant roles in determining the true answers from the large answer candidate pool. The experimental results show that across two testing datasets, our QA system achieves an 18%~54% improvement under F_1 metric, compared with various existing QA systems.

In collaborative environments, members may try to acquire similar information on the web in order to gain knowledge in one domain. For example, in a company several departments may successively need to buy business intelligence software and employees from these departments may have studied online about different business intelligence tools and their features independently. It will be productive to get them connected and share learned knowledge. We investigate fine-grained knowledge sharing in collaborative environments. We propose to analyze members' web surfing data to summarize the fine-grained knowledge acquired by them. A two-step framework is proposed for mining fine-grained knowledge: (1) web surfing data is clustered into tasks by a nonparametric generative model; (2) a novel discriminative infinite Hidden Markov Model is developed to mine fine-grained aspects in each task. Finally, the classic expert search method is applied to the mined results to find proper members for knowledge sharing. Experiments on web surfing data collected from our lab at UCSB and IBM show that the fine-grained aspect mining framework works as expected and outperforms baselines. When it is integrated with expert search, the search accuracy improves significantly, in comparison with applying the classic expert search method directly on web surfing data.

Collaborative networks are composed of experts who cooperate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his routing decision (i.e., where to transfer a task) is critical since it can significantly affect the completion time of a task. In this work, we attempt to deduce the cognitive process of task routing, and model the decision making of experts as a generative process where a routing decision is made based on mixed routing patterns.

The recent blossom of social network and communication services in both public and corporate settings have generated a staggering amount of network data of all kinds. Unlike the bio-networks and the chemical compound graph data often used in traditional network mining and analysis, the new network data grown out of the social applications are characterized by their rich attributes, high heterogeneity, enormous sizes and complex patterns of various semantic meanings, all of which have posed significant research challenges to the graph/network mining community. In this tutorial, we aim to examine some recent advances in network mining and analysis for social applications, covering a diverse collection of methodologies and applications from the perspectives of event, relationship, collaboration, and network pattern. We would present the problem settings, the challenges, the recent research advances and some future directions for each perspective. Topics include but are not limited to correlation mining, iceberg finding, anomaly detection, relationship discovery, information flow, task routing, and pattern mining.

Uncovering subgraphs with an abnormal distribution of attributes reveals much insight into network behaviors. For example in social or communication networks, diseases or intrusions usually do not propagate uniformly, which makes it critical to find anomalous regions with high concentrations of a specific disease or intrusion. In this paper, we introduce a probabilistic model to identify anomalous subgraphs containing a significantly different percentage of a certain vertex attribute, such as a specific disease or an intrusion, compared to the rest of the graph. Our framework, gAnomaly, models generative processes of vertex attributes and divides the graph into regions that are governed by background and anomaly processes. Two types of regularizers are employed to smoothen the regions and to facilitate vertex assignment. We utilize deterministic annealing EM to learn the model parameters, which is less initialization-dependent and better at avoiding local optima. In order to find fine-grained anomalies, an iterative procedure is further proposed. Experiments show gAnomaly outperforms a state-of-the-art algorithm at uncovering anomalous subgraphs in attributed graphs.

Querying complex graph databases such as knowledge graphs is a challenging task for non-professional users. In this demo, we present SLQ, a user-friendly graph querying system enabling schemales and structures graph querying, where a user need not describe queries precisely as required by most databases. SLQ system combines searching and ranking: it leverages a set of transformation functions, including abbreviation, ontology, synonym, etc., that map keywords and linkages from a query to their matches in a data graph, based on an automatically learned ranking model. To help users better understand search results at different levels of granularity, it supports effective result summarization with "drill-down" and "roll-up" operations. Better still, the architecture of SLQ is elastic for new transformation functions, query logs and user feedback, to iteratively refine the ranking model. SLQ significantly improves the usability of graph querying. This demonstration highlights (1) SLQ can automatically learn an effective ranking model, without assuming manually labeled training examples, (2) it can efficiently return top ranked matches over noisy, large data graphs, (3) it can summarize the query matches to help users easily access, explore and understand query results, and (4) its GUI can interact with users to help them construct queries, explore data graphs and inspect matches in a user-friendly manner.

Querying complex graph databases such as knowledge graphs is a challenging task for non-professional users. Due to their complex schemas and variational information descriptions, it becomes very hard for users to formulate a query that can be properly processed by the existing systems. We argue that for a user-friendly graph query engine, it must support various kinds of transformations such as synonym, abbreviation, and ontology. Furthermore, the derived query results must be ranked in a principled manner. In this paper, we introduce a novel framework enabling schemaless and structureless graph querying (SLQ), where a user need not describe queries precisely as required by most databases. The query engine is built on a set of transformation functions that automatically map keywords and linkages from a query to their matches in a graph. It automatically learns an effective ranking model, without assuming manually labeled training examples, and can efficiently return top ranked matches using graph sketch and belief propagation. The architecture of SLQ is elastic for "plug-in" new transformation functions and query logs. Our experimental results show that this new graph querying paradigm is promising: It identifies high-quality matches for both keyword and graph queries over real-life knowledge graphs, and outperforms existing methods significantly in terms of effectiveness and efficiency.

Millions of users share their opinions on Twitter, making it a valuable platform for tracking and analyzing public sentiment. Such tracking and analysis can provide critical information for decision making in various domains. Therefore it has attracted attention in both academia and industry. Previous research mainly focused on modeling and tracking public sentiment. In this work, we move one step further to interpret sentiment variations. We observed that emerging topics (named foreground topics) within the sentiment variation periods are highly related to the genuine reasons behind the variations. Based on this observation, we propose a Latent Dirichlet Allocation (LDA) based model, Foreground and Background LDA (FB-LDA), to distill foreground topics and filter out longstanding background topics. These foreground topics can give potential interpretations of the sentiment variations. To further enhance the readability of the mined reasons, we select the most representative tweets for foreground topics and develop another generative model called Reason Candidate and Background LDA (RCB-LDA) to rank them with respect to their “popularity” within the variation period. Experimental results show that our methods can effectively find foreground topics and rank reason candidates. The proposed models can also be applied to other tasks such as finding topic differences between two sets of documents.

Biclustering is crucial in finding co-expressed genes and their associated conditions in gene expression data. While various biclustering algorithms (e.g., combinatorial, probabilistic modelling, and matrix factorization) have been proposed and constantly improved in the past decade, data noise and bicluster overlaps make biclustering a still challenging task. It becomes difficult to further improve biclustering performance, without resorting to a new approach. Inspired by the recent progress in unsupervised feature learning using deep neural networks, in this work, we propose a novel model for biclustering, named Auto Decoder (AD), by relating biclusters to features and leveraging a neural network that is able to automatically learn features from the input data. To suppress severe noise present in gene expression data, we introduce a non-uniform signal recovery mechanism: Instead of reconstructing the whole input data to capture the bicluster patterns, AD weighs the zero and non-zero parts of the input data differently and is more flexible in dealing with different types of noise. AD is also properly regularized to deal with bicluster overlaps. To the best of our knowledge, this is the first biclustering algorithm that leverages neural network techniques to recover overlapped biclusters hidden in noisy gene expression data. We compared our approach with four state-of-the-art biclustering algorithms on both synthetic and real datasets. On three out of the four real datasets, AD significantly outperforms the other approaches. On controlled synthetic datasets, AD performs the best when noise level is beyond 15%.

Online reviews have been popularly adopted in many applications. Since they can either promote or harm the reputation of a product or a service, buying and selling fake reviews becomes a profitable business and a big threat. In this paper, we introduce a very simple, but powerful review spamming technique that could fail the existing feature-based detection algorithms easily. It uses one truthful review as a template, and replaces its sentences with those from other reviews in a repository. Fake reviews generated by this mechanism are extremely hard to detect: Both the state-of-the-art computational approaches and human readers acquire an error rate of 35%-48%, just slightly better than a random guess. While it is challenging to detect such fake reviews, we have made solid progress in suppressing them. A novel defense method that leverages the difference of semantic flows between synthetic and truthful reviews is developed, which is able to reduce the detection error rate to approximately 22%, a significant improvement over the performance of existing approaches. Nevertheless, it is still a challenging research task to further decrease the error rate.