Inferring contextual information such as demographics from historical transactions is valuable to public agencies and businesses. Existing methods are data-hungry and do not work well when the available records of transactions are sparse. We consider here specifically inference of demographic information using limited historical grocery transactions from a few random trips that a typical business or public service organization may see. We propose a novel method calledDemoMotifto build a network model from heterogeneous data and identify subgraph patterns (i.e., motifs) that enable us to infer demographic attributes. We then design a novel motif context selection algorithm to find specific node combinations significant to certain demographic groups. Finally, we learn representations of households using these selected motif instances as context, and employ a standard classifier (e.g., SVM) for inference. For evaluation purposes, we use three real-world consumer datasets, spanning different regions and time periods in the U.S. We evaluate the framework for predicting three attributes: ethnicity, seniority of household heads, and presence of children. Extensive experiments and case studies demonstrate thatDemoMotifis capable of inferring household demographics using only a small number (e.g., fewer than 10) of random grocery trips, significantly outperforming the state-of-the-art.

Real-world deployment of federated learning requires orchestrating clients with widely varied compute resources, from strong enterprise-grade devices in data centers to weak mobile and Web-of-Things devices. Prior works have attempted to downscale large models for weak devices and aggregate shared parts among heterogeneous models. A typical architectural assumption is that there are equally many strong and weak devices. In reality, however, we often encounter resource skew where a few (1 or 2) strong devices hold substantial data resources, alongside many weak devices. This poses challenges-the unshared portion of the large model rarely receives updates or gains benefits from weak collaborators.

Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different activity names often have shared structures. For example, "open door" and "open fridge" both have "open" as the action; "kicking soccer ball" and "playing tennis ball" both have "ball" as the object. Such shared structures in label names can be translated to the similarity in sensory data and modeling common structures would help uncover knowledge across different activities, especially for activities with limited samples. In this paper, we propose SHARE, a HAR framework that takes into account shared structures of label names for different activities. To exploit the shared structures, SHARE comprises an encoder for extracting features from input sensory time series and a decoder for generating label names as a token sequence. We also propose three label augmentation techniques to help the model more effectively capture semantic structures across activities, including a basic token-level augmentation, and two enhanced embedding-level and sequence-level augmentations utilizing the capabilities of pre-trained models. SHARE outperforms state-of-the-art HAR models in extensive experiments on seven HAR benchmark datasets. We also evaluate in few-shot learning and label imbalance settings and observe even more significant performance gap.

State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes of interest. This is never easy to meet in practice when human explore new, large corpora without complete pictures. In this paper, we work on a novel yet important problem of weakly supervised open-world text classification, where supervision is only needed for a few examples from a few known classes and the machine should handle both known and unknown classes in test time. General open-world classification has been studied mostly using image classification; however, existing methods typically assume the availability of sufficient known-class supervision and strong unknown-class prior knowledge (e.g., the number and/or data distribution). We propose a novel framework øur that lifts those strong assumptions. Specifically, it follows an iterative process of (a) clustering text to new classes, (b) mining and ranking indicative words for each class, and (c) merging redundant classes by using the overlapped indicative words as a bridge. Extensive experiments on 7 popular text classification datasets demonstrate that øur outperforms strong baselines consistently with a large margin, attaining 23.33% greater average absolute macro-F1 over existing approaches across all datasets. Such competent accuracy illuminates the practical potential of further reducing human effort for text classification.

Personalized natural language generation for explainable recommendations plays a key role in justifying why a recommendation might match a user's interests. Existing models usually control the generation process by aspect planning. While promising, these aspect-planning methods struggle to generate specific information correctly, which prevents generated explanations from being convincing. In this paper, we claim that introducing lexical constraints can alleviate the above issues. We propose a model, UCEpic, that generates high-quality personalized explanations for recommendation results by unifying aspect planning and lexical constraints in an insertion-based generation manner.

Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still struggle to model cold-start items or transfer knowledge to new datasets. In this paper, we propose to model user preferences and item features as language representations that can be generalized to new items and datasets. To this end, we present a novel framework, named Recformer, which effectively learns language representations for sequential recommendation. Specifically, we propose to formulate an item as a "sentence" (word sequence) by flattening item key-value attributes described by text so that an item sequence for a user becomes a sequence of sentences. For recommendation, Recformer is trained to understand the "sentence" sequence and retrieve the next "sentence". To encode item sequences, we design a bi-directional Transformer similar to the model Longformer but with different embedding layers for sequential recommendation. For effective representation learning, we propose novel pretraining and finetuning methods which combine language understanding and recommendation tasks. Therefore, Recformer can effectively recommend the next item based on language representations. Extensive experiments conducted on six datasets demonstrate the effectiveness of Recformer for sequential recommendation, especially in low-resource and cold-start settings.

Traditional federated classification methods, even those designed for non-IID clients, assume that each client annotates its local data with respect to the same universal class set. In this paper, we focus on a more general yet practical setting, non-identical client class sets, where clients focus on their own (different or even non-overlapping) class sets and seek a global model that works for the union of these classes. If one views classification as finding the best match between representations produced by data/label encoder, such heterogeneity in client class sets poses a new significant challenge-local encoders at different clients may operate in different and even independent latent spaces, making it hard to aggregate at the server. We propose a novel framework, FedAlign1, to align the latent spaces across clients from both label and data perspectives. From a label perspective, we leverage the expressive natural language class names as a common ground for label encoders to anchor class representations and guide the data encoder learning across clients. From a data perspective, during local training, we regard the global class representations as anchors and leverage the data points that are close/far enough to the anchors of locally-unaware classes to align the data encoders across clients. Our theoretical analysis of the generalization performance and extensive experiments on four real-world datasets of different tasks confirm that FedAlign outperforms various state-of-the-art (non-IID) federated classification methods.

Time-series data contains temporal order information that can guide representation learning for predictive end tasks (e.g., classification, regression). Recently, there are some attempts to leverage such order information to first pre-train time-series models by reconstructing time-series values of randomly masked time segments, followed by an end-task fine-tuning on the same dataset, demonstrating improved end-task performance. However, this learning paradigm decouples data reconstruction from the end task. We argue that the representations learnt in this way are not informed by the end task and may, therefore, be sub-optimal for the end-task performance. In fact, the importance of different timestamps can vary significantly in different end tasks. We believe that representations learnt by reconstructing important timestamps would be a better strategy for improving end-task performance. In this work, we propose TARNet, Task-Aware Reconstruction Network, a new model using Transformers to learn task-aware data reconstruction that augments end-task performance. Specifically, we design a data-driven masking strategy that uses self-attention score distribution from end-task training to sample timestamps deemed important by the end task. Then, we mask out data at those timestamps and reconstruct them, thereby making the reconstruction task-aware. This reconstruction task is trained alternately with the end task at every epoch, sharing parameters in a single model, allowing the representation learnt through reconstruction to improve end-task performance. Extensive experiments on tens of classification and regression datasets show that TARNet significantly outperforms state-of-the-art baseline models across all evaluation metrics.

Scientific discoveries and studies about our physical world have long benefited from large-scale and planetary sensing, from weather forecasting to wildfire monitoring. However, the limited deployment of sensors in the environment due to cost or physical access constraints has lagged behind our ever-growing need for increased data coverage and higher resolution, impeding timely and precise monitoring and understanding of the environment. Therefore, we seek to extend the spatial coverage of analysis based on existing sensory data, that is, to "generate" data for locations where no historical data exists. This problem is fundamentally different and more challenging than the traditional spatio-temporal imputation that assumes data for any particular location are only partially missing across time. Inspired by the success of Generative Adversarial Network (GAN) in imputation, we propose a novel ESC-GAN. We observe that there are local patterns in nearby locations, as well as trends in a global manner (e.g., temperature drops as altitude increases regardless of the location). As local patterns may exhibit at different scales (from meters to kilometers), we employ a multi-branch generator to aggregate information of different granularity. More specifically, each branch in the generator contains 1) randomly masked 3D partial convolutions at different resolutions to capture the local patterns and 2) global attention modules for global similarity. Next, we adversarially train a 3D convolution-based discriminator to distinguish the generator's output from the ground truth. Extensive experiments on three geo-sensor datasets demonstrate that ESC-GAN outperforms state-of-the-art methods on extending spatial coverage and also achieves the best results on a traditional spatio-temporal imputation task.

Automatic extraction of product attributes from their textual descriptions is essential for online shopper experience. One inherent challenge of this task is the emerging nature of e-commerce products — we see new types of products with their unique set of new attributes constantly. Most prior works on this matter mine new values for a set of known attributes but cannot handle new attributes that arose from constantly changing data. In this work, we study the attribute mining problem in an open-world setting to extract novel attributes and their values. Instead of providing comprehensive training data, the user only needs to provide a few examples for a few known attribute types as weak supervision. We propose a principled framework that first generates attribute value candidates and then groups them into clusters of attributes. The candidate generation step probes a pre-trained language model to extract phrases from product titles. Then, an attribute-aware fine-tuning method optimizes a multitask objective and shapes the language model representation to be attribute-discriminative. Finally, we discover new attributes and values through the self-ensemble of our framework, which handles the open-world challenge. We run extensive experiments on a large distantly annotated development set and a gold standard human-annotated test set that we collected. Our model significantly outperforms strong baselines and can generalize to unseen attributes and product types.

Identifying and understanding quality phrases from context is a fundamental task in text mining. The most challenging part of this task arguably lies in uncommon, emerging, and domain-specific phrases. The infrequent nature of these phrases significantly hurts the performance of phrase mining methods that rely on sufficient phrase occurrences in the input corpus. Context-aware tagging models, though not restricted by frequency, heavily rely on domain experts for either massive sentence-level gold labels or handcrafted gazetteers. In this work, we propose UCPhrase, a novel unsupervised context-aware quality phrase tagger. Specifically, we induce high-quality phrase spans as silver labels from consistently co-occurring word sequences within each document. Compared with typical context-agnostic distant supervision based on existing knowledge bases (KBs), our silver labels root deeply in the input domain and context, thus having unique advantages in preserving contextual completeness and capturing emerging, out-of-KB phrases. Training a conventional neural tagger based on silver labels usually faces the risk of overfitting phrase surface names. Alternatively, we observe that the contextualized attention maps generated from a transformer-based neural language model effectively reveal the connections between words in a surface-agnostic way. Therefore, we pair such attention maps with the silver labels to train a lightweight span prediction model, which can be applied to new input to recognize (unseen) quality phrases regardless of their surface names or frequency. Thorough experiments on various tasks and datasets, including corpus-level phrase ranking, document-level keyphrase extraction, and sentence-level phrase tagging, demonstrate the superiority of our design over state-of-the-art pre-trained, unsupervised, and distantly supervised methods.

Aspect classification, identifying aspects of text segments, facilitates numerous applications, such as sentiment analysis and review summarization.To alleviate the extensive human effort required by existing aspect classification methods, in this paper, we focus on a weakly supervised setting-the model input only contains domainspecific raw texts and a few seed words per pre-defined aspect.We identify a unique challenge here as to how to classify texts without any pre-defined aspects.The existence of this kind of "misc" aspect text segments is very common in review corpora.It is difficult, even for domain experts, to nominate seed words for the "misc" aspect, which makes existing seed-driven text classification methods not applicable.Therefore, we propose to jointly model pre-defined aspects and the "misc" aspect through a novel framework, ARYA.It enables mutual enhancements between pre-defined aspects and the "misc" aspect via iterative classifier training and seed set updating.Specifically, it trains a classifier for pre-defined aspects and then leverages it to induce the supervision for the "misc" aspect.The prediction results of the "misc" aspect are later utilized to further filter the seed word selections for pre-defined aspects.Experiments in three domains demonstrate the superior performance of our proposed framework, as well as the necessity and importance of properly modeling the "misc" aspect.

We propose AutoName, an unsupervised framework that extracts a name for a set of query entities from a large-scale text corpus. Entity-set naming is useful in many tasks related to natural language processing and information retrieval such as session-based and conversational information seeking. Previous studies mainly extract set names from knowledge bases which provide highly reliable entity relations, but suffer from limited coverage of entities and set names that represent broad semantic classes. To address these problems, AutoName generates hypernym-anchored candidate phrases via probing a pre-trained language model and the entities' context in documents. Phrases are then clustered to identify ones that describe common concepts among query entities. Finally, AutoName ranks refined phrases based on the co-occurrences of their words with query entities and the conceptual integrity of their respective clusters. We built a new benchmark dataset for this task, consisting of 130 entity sets with name labels. Experimental results show that AutoName generates coherent and meaningful set names and significantly outperforms all baselines.

Text categorization is an essential task in Web content analysis. Considering the ever-evolving Web data and new emerging categories, instead of the laborious supervised setting, in this paper, we focus on the minimally-supervised setting that aims to categorize documents effectively, with a couple of seed documents annotated per category. We recognize that texts collected from the Web are often structure-rich, i.e., accompanied by various metadata. One can easily organize the corpus into a text-rich network, joining raw text documents with document attributes, high-quality phrases, label surface names as nodes, and their associations as edges. Such a network provides a holistic view of the corpus’ heterogeneous data sources and enables a joint optimization for network-based analysis and deep textual model training. We therefore propose a novel framework for minimally supervised categorization by learning from the text-rich network. Specifically, we jointly train two modules with different inductive biases – a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning. Each module generates pseudo training labels from the unlabeled document set, and both modules mutually enhance each other by co-training using pooled pseudo labels. We test our model on two real-world datasets. On the challenging e-commerce product categorization dataset with 683 categories, our experiments show that given only three seed documents per category, our framework can achieve an accuracy of about 92%, significantly outperforming all compared methods; our accuracy is only less than 2% away from the supervised BERT model trained on about 50K labeled documents.

Unstructured scientific text, in various forms of textual artifacts, including manuscripts, publications, patents, and proposals, is used to store the tremendous wealth of knowledge discovered after weeks, months, and years, developing hypotheses, working in the lab or clinic, and analyzing results. A grand challenge on data mining research is to develop effective methods for transforming the scientific text into well-structured forms (e.g., ontology, taxonomy, knowledge graphs), so that machine intelligent systems can build on them for hypothesis generation and validation. In this tutorial, we provide a comprehensive overview on recent research and development in this direction. First, we introduce a series of text mining methods that extract phrases, entities, scientific concepts, relations, claims, and experimental evidence. Then we discuss methods that construct and learn from scientific knowledge graphs for accurate search, document classification, and exploratory analysis. Specifically, we focus on scalable, effective, weakly supervised methods that work on text in sciences (e.g., chemistry, biology).

The automated construction of topic taxonomies can benefit numerous applications, including web search, recommendation, and knowledge discovery. One of the major advantages of automatic taxonomy construction is the ability to capture corpus-specific information and adapt to different scenarios. To better reflect the characteristics of a corpus, we take the meta-data of documents into consideration and view the corpus as a text-rich network. In this paper, we propose NetTaxo, a novel automatic topic taxonomy construction framework, which goes beyond the existing paradigm and allows text data to collaborate with network structure. Specifically, we learn term embeddings from both text and network as contexts. Network motifs are adopted to capture appropriate network contexts. We conduct an instance-level selection for motifs, which further refines term embedding according to the granularity and semantics of each taxonomy node. Clustering is then applied to obtain sub-topics under a taxonomy node. Extensive experiments on two real-world datasets demonstrate the superiority of our method over the state-of-the-art, and further verify the effectiveness and importance of instance-level motif selection.

Real-world data exists largely in the form of unstructured texts. A grand challenge on data mining research is to develop effective and scalable methods that may transform unstructured text into structured knowledge. Based on our vision, it is highly beneficial to transform such text into structured heterogeneous information networks, on which actionable knowledge can be generated based on the user's need. In this tutorial, we provide a comprehensive overview on recent research and development in this direction. First, we introduce a series of effective methods that construct heterogeneous information networks from massive, domain-specific text corpora. Then we discuss methods that mine such text-rich networks based on the user's need. Specifically, we focus on scalable, effective, weakly supervised, language-agnostic methods that work on various kinds of text. We further demonstrate, on real datasets (including news articles, scientific publications, and product reviews), how information networks can be constructed and how they can assist further exploratory analysis.

Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e., entity arguments and a predicate string to describe their relation) from sentences. These relation tuples are not confined to a predefined schema for the relations of interests. However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions. In this paper, we propose a novel Open IE system, called ReMine, which integrates local context signals and global structural signals in a unified, distant-supervision framework. Leveraging facts from external knowledge bases as supervision, the new system can be applied to many different domains to facilitate sentence-level tuple extractions using corpus-level statistics. Our system operates by solving a joint optimization problem to unify (1) segmenting entity/relation phrases in individual sentences based on local context; and (2) measuring the quality of tuples extracted from individual sentences with a translating-based objective. Learning the two subtasks jointly helps correct errors produced in each subtask so that they can mutually enhance each other. Experiments on two real-world corpora from different domains demonstrate the effectiveness, generality, and robustness of ReMine when compared to state-of-the-art open IE systems.

Today's society is immersed in a wealth of text data, ranging from news articles, to social media, research literature, medical records, and corporate reports. A grand challenge of data science and engineering is to develop effective and scalable methods to extract structures and knowledge from massive text data to satisfy diverse applications, without extensive, corpus-specific human annotations. In this tutorial, we show that TextCube provides a critical information organization structure that will satisfy such an information need. We overview a set of recently developed data-driven methods that facilitate automated construction of TextCubes from massive, domain-specific text corpora, and show that TextCubes so constructed will enhance text exploration and analysis for various applications. We focus on new TextCube construction methods that are scalable, weakly-supervised, domain-independent, language-agnostic, and effective (i.e., generating quality TextCubes from large corpora of various domains). We will demonstrate with real datasets (including news articles, scientific publications, and product reviews) on how TextCubes can be constructed to assist multidimensional analysis of massive text corpora.

Recent years have witnessed a widespread increase of rumor news generated by humans and machines. Therefore, tools for investigating rumor news have become an urgent necessity. One useful function of such tools is to see ways a specific topic or event is represented by presenting different points of view from multiple sources. In this paper, we propose Maester, a novel agreement-aware search framework for investigating rumor news. Given an investigative question, Maester will retrieve related articles to that question, assign and display top articles from agree, disagree, and discuss categories to users. Splitting the results into these three categories provides the user a holistic view towards the investigative question. We build Maester based on the following two key observations: (1) relatedness can commonly be determined by keywords and entities occurring in both questions and articles, and (2) the level of agreement between the investigative question and the related news article can often be decided by a few key sentences. Accordingly, we use gradient boosting tree models with keyword/entity matching features for relatedness detection, and leverage recurrent neural network to infer the level of agreement. Our experiments on the Fake News Challenge (FNC) dataset demonstrate up to an order of magnitude improvement of Maester over the original FNC winning solution, for agreement-aware search.

Literature search is critical for any scientific research. Different from Web or general domain search, a large portion of queries in scientific literature search are entity-set queries, that is, multiple entities of possibly different types. Entity-set queries reflect user's need for finding documents that contain multiple entities and reveal inter-entity relationships and thus pose non-trivial challenges to existing search algorithms that model each entity separately. However, entity-set queries are usually sparse (i.e., not so repetitive), which makes ineffective many supervised ranking models that rely heavily on associated click history. To address these challenges, we introduce SetRank, an unsupervised ranking framework that models inter-entity relationships and captures entity type information. Furthermore, we develop a novel unsupervised model selection algorithm, based on the technique of weighted rank aggregation, to automatically choose the parameter settings in SetRank without resorting to a labeled validation set. We evaluate our proposed unsupervised approach using datasets from TREC Genomics Tracks and Semantic Scholar's query log. The experiments demonstrate that SetRank significantly outperforms the baseline unsupervised models, especially on entity-set queries, and our model selection algorithm effectively chooses suitable parameter settings.

In the literature, two series of models have been proposed to address prediction problems including classification and regression. Simple models, such as generalized linear models, have ordinary performance but strong interpretability on a set of simple features. The other series, including tree-based models, organize numerical, categorical and high dimensional features into a comprehensive structure with rich interpretable information in the data. In this paper, we propose a novel Discriminative Pattern-based Prediction framework (DPPred) to accomplish the prediction tasks by taking their advantages of both effectiveness and interpretability. Specifically, DPPred adopts the concise discriminative patterns that are on the prefix paths from the root to leaf nodes in the tree-based models. DPPred selects a limited number of the useful discriminative patterns by searching for the most effective pattern combination to fit generalized linear models. Extensive experiments show that in many scenarios, DPPred provides competitive accuracy with the state-of-the-art as well as the valuable interpretability for developers and experts. In particular, taking a clinical application dataset as a case study, our DPPred outperforms the baselines by using only 40 concise discriminative patterns out of a potentially exponentially large set of patterns.

As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus and has various downstream applications including information extraction/retrieval, taxonomy construction, and topic modeling. Most existing methods rely on complex, trained linguistic analyzers, and thus likely have unsatisfactory performance on text corpora of new domains and genres without extra but expensive adaption. None of the state-of-the-art models, even data-driven models, is fully automated because they require human experts for designing rules or labeling phrases. In this paper, we propose a novel framework for automated phrase mining, AutoPhrase, which supports any language as long as a general knowledge base (e.g., Wikipedia) in that language is available, while benefiting from, but not requiring, a POS tagger. Compared to the state-of-the-art methods, AutoPhrase has shown significant improvements in both effectiveness and efficiency on five real-world datasets across different domains and languages. Besides, AutoPhrase can be extended to model single-word quality phrases.

Learning distributed node representations in networks has been attracting increasing attention recently due to its effectiveness in a variety of applications. Existing approaches usually study networks with a single type of proximity between nodes, which defines a single view of a network. However, in reality there usually exists multiple types of proximities between nodes, yielding networks with multiple views. This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations. During the voting process, an attention mechanism is introduced, which enables each node to focus on the most informative views. Experimental results on real-world networks show that the proposed approach outperforms existing state-of-the-art approaches for network representation learning with a single view and other competitive approaches with multiple views.

Periodicity is prevalent in physical world, and many events involve more than one periods, eg individual's mobility, tide pattern, and massive transportation utilization. Knowing the true periods of events can benefit a number of applications, such as traffic prediction, time-aware recommendation and advertisement, and anomaly detection. However, detecting multiple periods is a very challenging task due to not only the interwoven periodic patterns but also the low quality of event tracking records. In this paper, we study the problem of discovering all true periods and the corresponded occurring patterns of an event from a noisy and incomplete observation sequence. We devise a novel scoring function, by maximizing which we can identify the true periodic patterns involved in the sequence. We prove that, however, optimizing the objective function is an NP-hard problem. To address this challenge, we develop a heuristic algorithm named Timeslot Coverage Model (TiCom), for identifying the periods and periodic patterns approximately. The results of extensive experiments on both synthetic and real-life datasets show that our model outperforms the state-of-the-art baselines significantly in various tasks, including period detection, periodic pattern identification, and anomaly detection.

Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is costly for a corpus of large scale. In this study, we propose a novel typed textual pattern structure, called meta pattern, which is extended to a frequent, informative, and precise subsequence pattern in certain context. We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise. Experiments demonstrate that our proposed framework discovers high-quality typed textual patterns efficiently from different genres of massive corpora and facilitates information extraction.

In today's computerized and information-based society, people are inundated with vast amounts of text data, ranging from news articles, social media post, scientific publications, to a wide range of textual information from various domains (corporate reports, advertisements, legal acts, medical reports). To turn such massive unstructured text data into structured, actionable knowledge, one of the grand challenges is to gain an understanding of the factual information (e.g., entities, attributes, relations) in the text.

Users often write reviews on different themes involving linguistic structures with complex sentiments. The sentiment polarity of a word can be different across themes. Moreover, contextual valence shifters may change sentiment polarity depending on the contexts that they appear in. Both challenges cannot be modeled effectively and explicitly in traditional sentiment analysis. Studying both phenomena requires multi-theme sentiment analysis at the word level, which is very interesting but significantly more challenging than overall polarity classification. To simultaneously resolve the multi-theme and sentiment shifting problems, we propose a data-driven framework to enable both capabilities: (1) polarity predictions of the same word in reviews of different themes, and (2) discovery and quantification of contextual valence shifters. The framework formulates multi-theme sentiment by factorizing the review sentiments with theme/word embeddings and then derives the shifter effect learning problem as a logistic regression. The improvement of sentiment polarity classification accuracy demonstrates not only the importance of multi-theme and sentiment shifting, but also effectiveness of our framework. Human evaluations and case studies further show the success of multi-theme word sentiment predictions and automatic effect quantification of contextual valence shifters.

Consecutive pattern mining aiming at finding sequential patterns substrings, is a special case of frequent pattern mining and has been played a crucial role in many real world applications, especially in biological sequence analysis, time series analysis, and network log mining. Approximations, including insertions, deletions, and substitutions, between strings are widely used in biological sequence comparisons. However, most existing string pattern mining methods only consider hamming distance without insertions/deletions (indels). Little attention has been paid to the general approximate consecutive frequent pattern mining under edit distance, potentially due to the high computational complexity, particularly on DNA sequences with billions of base pairs. In this paper, we introduce an efficient solution to this problem. We first formulate the Maximal Approximate Consecutive Frequent Pattern Mining (MACFP) problem that identifies substring patterns under edit distance in a long query sequence. Then, we propose a novel algorithm with linear time complexity to check whether the support of a substring pattern is above a predefined threshold in the query sequence, thus greatly reducing the computational complexity of MACFP. With this fast decision algorithm, we can efficiently solve the original pattern discovery problem with several indexing and searching techniques. Comprehensive experiments on sequence pattern analysis and a study on cancer genomics application demonstrate the effectiveness and efficiency of our algorithm, compared to several existing methods.

Pattern-based classification was originally proposed to improve the accuracy using selected frequent patterns, where many efforts were paid to prune a huge number of non-discriminative frequent patterns. On the other hand, tree-based models have shown strong abilities on many classification tasks since they can easily build high-order interactions between different features and also handle both numerical and categorical features as well as high dimensional features. By taking the advantage of both modeling methodologies, we propose a natural and effective way to resolve pattern-based classification by adopting discriminative patterns which are the prefix paths from root to nodes in tree-based models (e.g., random forest). Moreover, we further compress the number of discriminative patterns by selecting the most effective pattern combinations that fit into a generalized linear model. As a result, our discriminative pattern-based classification framework (DPClass) could perform as good as previous state-of-the-art algorithms, provide great interpretability by utilizing only very limited number of discriminative patterns, and predict new data extremely fast. More specifically, in our experiments, DPClass could gain even better accuracy by only using top-20 discriminative patterns. The framework so generated is very concise and highly explanatory to human experts.

Many text mining approaches adopt bag-of-words or $n$-grams models to represent documents. Looking beyond just the words, fiie, the explicit surface forms, in a document can improve a computer's understanding of text. Being aware of this, researchers have proposed concept-based models that rely on a human-curated knowledge base to incorporate other related concepts in the document representation. But these methods are not desirable when applied to vertical domains (eg, literature, enterprise, etc) due to low coverage of in-domain concepts in the general knowledge base and interference from out-of-domain concepts. In this paper, we propose a data-driven model named Latent Keyphrase Inference LAKI) that represents documents with a vector of closely related domain keyphrases instead of single words or existing concepts in the knowledge base. We show that given a corpus of in-domain documents, topical content units can be learned for each domain keyphrase, which enables a computer to do smart inference to discover latent document keyphrases, going beyond just explicit mentions. Compared with the state-of-art document representation approaches, LAKI fills the gap between bag-of-words and concept-based models by using domain keyphrases as the basic representation unit. It removes dependency on a knowledge base while providing, with keyphrases, readily interpretable representations. When evaluated against 8 other methods on two text mining tasks over two corpora, LAKI outperformed all.

Many types of human mobility data, such as flows of taxicabs, card swiping data of subways, bike trip data and Call Details Records (CDR), can be modeled by a Spatio-Temporal Graph (STG). STG is a directed graph in which vertices and edges are associated with spatio-temporal properties (e.g. the traffic flow on a road and the geospatial location of an intersection). In this paper, we instantly detect interesting phenomena, entitled black holes and volcanos, from an STG. Specifically, a black hole is a subgraph (of an STG) that has the overall inflow greater than the overall outflow by a threshold, while a volcano is a subgraph with the overall outflow greater than the overall inflow by a threshold (detecting volcanos from an STG is proved to be equivalent to the detection of black holes). The online detection of black holes/volcanos can timely reflect anomalous events, such as disasters, catastrophic accidents, and therefore help keep public safety. The patterns of black holes/volcanos and the relations between them reveal human mobility patterns in a city, thus help formulate a better city planning or improve a system's operation efficiency. Based on a well-designed STG index, we propose a two-step black hole detection algorithm: The first step identifies a set of candidate grid cells to start from; the second step expands an initial edge in a candidate cell to a black hole and prunes other candidate cells after a black hole is detected. Then, we adapt this detection algorithm to a continuous black hole detection scenario. We evaluate our method based on Beijing taxicab data and the bike trip data in New York, finding urban anomalies and human mobility patterns.

Text data are ubiquitous and play an essential role in big data applications. However, text data are mostly unstructured. Transforming unstructured text into structured units (e.g., semantically meaningful phrases) will substantially reduce semantic ambiguity and enhance the power and efficiency at manipulating such data using database technology. Thus mining quality phrases is a critical research problem in the field of databases. In this paper, we propose a new framework that extracts quality phrases from text corpora integrated with phrasal segmentation. The framework requires only limited training but the quality of phrases so generated is close to human judgment. Moreover, the method is scalable: both computation time and required space grow linearly as corpus size increases. Our experiments on large text corpora demonstrate the quality and efficiency of the new method.

A recent area in which recommender systems have shown their value is in online discussion forums and question-answer sites. Earlier work in this space has focused on the problem of matching participants to opportunities but has not adequately addressed the problem that in these social contexts, multiple dimensions of constraints must be satisfied, including limitations on capacity and minimal requirements for expertise. In this work, we propose such a constrained question recommendation problem with load balance constraints in discussion forums and use flow based model to generate the optimal solution. In particular, to address the introduced computation complexity, we investigate the concept of submodularity of the objective function and propose a specific submodular method to give an approximated solution. We present experiments conducted on two Massive Open Online Course (MOOC) discussion forum datasets, and demonstrate the effectiveness and efficiency of our submodular method in solving constrained question recommendation tasks.

Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image tagging, and web search. Machine learning techniques, referred to as learning-to-match in this paper, have been successfully applied to the problems. Among them, a class of state-of-the-art methods, named feature-based matrix factorization, formalize the task as an extension to matrix factorization by incorporating auxiliary features into the model. Unfortunately, making those algorithms scale to real world problems is challenging, and simple parallelization strategies fail due to the complex cross talking patterns between sub-tasks. In this paper, we tackle this challenge with a novel parallel and efficient algorithm. Our algorithm, based on coordinate descent, can easily handle hundreds of millions of instances and features on a single machine. The key recipe of this algorithm is an iterative relaxation of the objective to facilitate parallel updates of parameters, with guaranteed convergence on minimizing the original objective function. Experimental results demonstrate that the proposed method is effective on a wide range of matching problems, with efficiency significantly improved upon the baselines while accuracy retained unchanged.

This paper instantly infers the gas consumption and pollution emission of vehicles traveling on a city's road network in a current time slot, using GPS trajectories from a sample of vehicles (e.g., taxicabs). The knowledge can be used to suggest cost-efficient driving routes as well as identifying road segments where gas has been wasted significantly. The instant estimation of the emissions from vehicles can enable pollution alerts and help diagnose the root cause of air pollution in the long run. In our method, we first compute the travel speed of each road segment using the GPS trajectories received recently. As many road segments are not traversed by trajectories (i.e., data sparsity), we propose a Travel Speed Estimation (TSE) model based on a context-aware matrix factorization approach. TSE leverages features learned from other data sources, e.g., map data and historical trajectories, to deal with the data sparsity problem. We then propose a Traffic Volume Inference (TVI) model to infer the number of vehicles passing each road segment per minute. TVI is an unsupervised Bayesian Network that incorporates multiple factors, such as travel speed, weather conditions and geographical features of a road. Given the travel speed and traffic volume of a road segment, gas consumption and emissions can be calculated based on existing environmental theories. We evaluate our method based on extensive experiments using GPS trajectories generated by over 32,000 taxis in Beijing over a period of two months. The results demonstrate the advantages of our method over baselines, validating the contribution of its components and finding interesting discoveries for the benefit of society.