Recognizing the various aliases of an entity is a critical task for many applications, including Web search, recommendation system, and e-discovery. The goal of this paper is to accurately identify entity aliases, especially the long tail ones in the unstructured data. Our solution GRIAS (abbr. for a Graph-based framework for discovering entity Aliases) is motivated by the entity relationships collected from both the structured and unstructured data. These relationships help to build an entity-relation graph, and the graph-based similarity is calculated between an entity and its alias candidates which are first chosen by our proposed candidate selection method. Extensive experimental results on two real-world datasets demonstrate both the effectiveness and efficiency of the proposed framework.

The distribution difference among multiple domains has been exploited for cross-domain text categorization in recent years. Along this line, we show two new observations in this study. First, the data distribution difference is often due to the fact that different domains use different index words to express the same concept. Second, the association between the conceptual feature and the document class can be stable across domains. These two observations actually indicate the distinction and commonality across domains. Inspired by the above observations, we propose a generative statistical model, named Collaborative Dual-PLSA (CD-PLSA), to simultaneously capture both the domain distinction and commonality among multiple domains. Different from Probabilistic Latent Semantic Analysis (PLSA) with only one latent variable, the proposed model has two latent factors y and z, corresponding to word concept and document class, respectively. The shared commonality intertwines with the distinctions over multiple domains, and is also used as the bridge for knowledge transformation. An Expectation Maximization (EM) algorithm is developed to solve the CD-PLSA model, and further its distributed version is exploited to avoid uploading all the raw data to a centralized location and help to mitigate privacy concerns. After the training phase with all the data from multiple domains we propose to refine the immediate outputs using only the corresponding local data. In summary, we propose a two-phase method for cross-domain text classification, the first phase for collaborative training with all the data, and the second step for local refinement. Finally, we conduct extensive experiments over hundreds of classification tasks with multiple source domains and multiple target domains to validate the superiority of the proposed method over existing state-of-the-art methods of supervised and transfer learning. It is noted to mention that as shown by the experimental results CD-PLSA for the collaborative training is more tolerant of distribution differences, and the local refinement also gains significant improvement in terms of classification accuracy.

The distribution difference among multiple domains has been exploited for cross-domain text categorization in recent years. Along this line, we show two new observations in this study. First, the data distribution difference is often due to the fact that different domains use different index words to express the same concept. Second, the association between the conceptual feature and the document class can be stable across domains. These two observations actually indicate the distinction and commonality across domains. Inspired by the above observations, we propose a generative statistical model, named Collaborative Dual-PLSA (CD-PLSA), to simultaneously capture both the domain distinction and commonality among multiple domains. Different from Probabilistic Latent Semantic Analysis (PLSA) with only one latent variable, the proposed model has two latent factors y and z, corresponding to word concept and document class, respectively. The shared commonality intertwines with the distinctions over multiple domains, and is also used as the bridge for knowledge transformation. An Expectation Maximization (EM) algorithm is developed to solve the CD-PLSA model, and further its distributed version is exploited to avoid uploading all the raw data to a centralized location and help to mitigate privacy concerns. After the training phase with all the data from multiple domains we propose to refine the immediate outputs using only the corresponding local data. In summary, we propose a two-phase method for cross-domain text classification, the first phase for collaborative training with all the data, and the second step for local refinement. Finally, we conduct extensive experiments over hundreds of classification tasks with multiple source domains and multiple target domains to validate the superiority of the proposed method over existing state-of-the-art methods of supervised and transfer learning. It is noted to mention that as shown by the experimental results CD-PLSA for the collaborative training is more tolerant of distribution differences, and the local refinement also gains significant improvement in terms of classification accuracy.

The goal of focused crawling is to use limited resources to effectively discover web pages related to a specific topic rather than downloading all accessible web documents. The major challenge in focused crawling is how to effectively determine each hyperlink's capability of leading to target pages. To compute this capability, we present a novel approach, called Navigational Rank (NR). In general, NR is a kind of two-step and two-direction credit propagation approach. Compared to existing methods, NR mainly has three advantages. First, NR is dynamically updated during the crawling progress, which can adapt to different website structures very well. Second, when the crawling seed is far away from the target pages, and the target pages only constitute a small portion of the whole website, NR shows a significant performance advantage. Third, NR computes each link's capability of leading to target pages by considering both the target and non-target pages it leads to. This global knowledge causes a better performance than only using target pages. We have performed extensive experiments for performance evaluation of the proposed approach using two groups of large-scale, real-world datasets from two different domains. The experimental results show that our approach is domain-independent and significantly outperforms the state-of-arts.

The distribution difference among multiple data domains has been considered for the cross-domain text classification problem. In this study, we show two new observations along this line. First, the data distribution difference may come from the fact that different domains use different key words to express the same concept. Second, the association between this conceptual feature and the document class may be stable across domains. These two issues are actually the distinction and commonality across data domains. Inspired by the above observations, we propose a generative statistical model, named Collaborative Dual-PLSA (CD-PLSA), to simultaneously capture both the domain distinction and commonality among multiple domains. Different from Probabilistic Latent Semantic Analysis (PLSA) with only one latent variable, the proposed model has two latent factors y and z, corresponding to word concept and document class respectively. The shared commonality intertwines with the distinctions over multiple domains, and is also used as the bridge for knowledge transformation. We exploit an Expectation Maximization (EM) algorithm to learn this model, and also propose its distributed version to handle the situation where the data domains are geographically separated from each other. Finally, we conduct extensive experiments over hundreds of classification tasks with multiple source domains and multiple target domains to validate the superiority of the proposed CD-PLSA model over existing state-of-the-art methods of supervised and transfer learning. In particular, we show that CD-PLSA is more tolerant of distribution differences.

We describe a method to extract style and branding elements from multiple web pages in a given site for content repurposing. Style and branding elements convey the values of the site owners effectively and connect with the target prospects. They are manifested through logos, graphical elements, background color, font styles, font colors and other illustrations. Our method automatically extracts color and image elements appearing frequently and prominently on multiple pages throughout the site. We rely on a DOM tree matching method to obtain the frequency of re-occurring elements and use relative sizes and positions of elements to determine the type of elements. Note that approximate locations of these elements provide an added clue to the content repurposing engine as to where to place the elements in the repurposed document. The obtained results show that the proposed method can efficiently extract style and branding elements with high accuracy.

We study what we call semi-defined classification, which deals with the categorization tasks where the taxonomy of the data is not well defined in advance. It is motivated by the real-world applications, where the unlabeled data may also come from some other unknown classes besides the known classes for the labeled data. Given the unlabeled data, our goal is to not only identify the instances belonging to the known classes, but also cluster the remaining data into other meaningful groups. It differs from traditional semi-supervised clustering in the sense that in semi-supervised clustering the supervision knowledge is too far from being representative of a target classification, while in semi-defined classification the labeled data may be enough to supervise the learning on the known classes. In this paper we propose the model of Double-latent-layered LDA (D-LDA for short) for this problem. Compared with LDA with only one latent variable y for word topics, D-LDA contains another latent variable z for (known and unknown) document classes. With this double latent layers consisting of y and z and the dependency between them, D-LDA directly injects the class labels into z to supervise the exploiting of word topics in y. Thus, the semi-supervised learning in D-LDA does not need the generation of pair wise constraints, which is required in most of the previous semi-supervised clustering approaches. We present the experimental results on ten different data sets for semi-defined classification. Our results are either comparable to (on one data sets), or significantly better (on the other nine data set) than the six compared methods, including the state-of-the-art semi-supervised clustering methods.

Cross-domain text categorization targets on adapting the knowledge learnt from a labeled source-domain to an unlabeled target-domain, where the documents from the source and target domains are drawn from different distributions. However, in spite of the different distributions in raw word features, the associations between word clusters (conceptual features) and document classes may remain stable across different domains. In this paper, we exploit these unchanged associations as the bridge of knowledge transformation from the source domain to the target domain by the nonnegative matrix tri-factorization. Specifically, we formulate a joint optimization framework of the two matrix tri-factorizations for the source and target domain data respectively, in which the associations between word clusters and document classes are shared between them. Then, we give an iterative algorithm for this optimization and theoretically show its convergence. The comprehensive experiments show the effectiveness of this method. In particular, we show that the proposed method can deal with some difficult scenarios where baseline methods usually do not perform well.

Ptolemy II is a component-based design and modeling environment. It has a polymorphic type system that supports both base types and structured types, such as arrays, records, and unions. This paper presents the extensions to the base type system that support structured types. In the base type system, all the types are organized into a type lattice, and type constraints in the form of inequalities can be solved efficiently over the lattice. We take a hierarchical and granular approach to add structured types to the lattice and extend the format of inequality constraints to allow arbitrary nesting of structured types. We also analyze the convergence of the constraint-solving algorithm on an infinite lattice after structured types are added. To show the application of structured types, we present two Ptolemy II models that have direct real-world background. The first one describes the workflow of a charity organization, and the second one implements part of the IEEE 802.11 specification. These models make extensive use of record and union types to represent structured information. © 2009 Wiley Periodicals, Inc.

Classification across different domains studies how to adapt a learning model from one domain to another domain which shares similar data characteristics. While there are a number of existing works along this line, many of them are only focused on learning from a single source domain to a target domain. In particular, a remaining challenge is how to apply the knowledge learned from multiple source domains to a target domain. Indeed, data from multiple source domains can be semantically related, but have different data distributions. It is not clear how to exploit the distribution differences among multiple source domains to boost the learning performance in a target domain. To that end, in this paper, we propose a consensus regularization framework for learning from multiple source domains to a target domain. In this framework, a local classifier is trained by considering both local data available in one source domain and the prediction consensus with the classifiers learned from other source domains. Moreover, we provide a theoretical analysis as well as an empirical study of the proposed consensus regularization framework. The experimental results on text categorization and image classification problems show the effectiveness of this consensus regularization learning method. Finally, to deal with the situation that the multiple source domains are geographically distributed, we also develop the distributed version of the proposed algorithm, which avoids the need to upload all the data to a centralized location and helps to mitigate privacy concerns.

In this paper, we focus on the automatic extraction and disambiguation of acronyms in large-scale organizational web pages, which is important but difficult due to the diversity of acronyms and the scale of organizational web pages. We propose two novel algorithms to address the key problems in acronym extraction and disambiguation: (1) An unsupervised ranking algorithm to automatically filter out the incorrect acronym-expansion pairs. Different from the existing approaches, our method does not require any hand-crafted rules; (2) A graph-based algorithm to disambiguate ambiguous acronyms, which leverages the hyperlinks of pages to facilitate the acronym disambiguation. We evaluate the proposed approaches using two large-scale, real-world datasets in two different domains. Our experimental results show that our approach is domain independent, and achieves higher precision and recall than the existing methods.

In this paper we present OfCourse, a vertical search engine for online course materials. These materials have the following characteristics: they are scattered very sparsely in the university Web sites; and are generated by the teachers with totally different HMTL templates and layouts. These characteristics impose some challenges for Web Classification (to identify the course materials) and Web Information Extraction (to extract course metadata, such as course title, time and ID) from the identified course homepages. Here, we describe our proposed method to tackle these challenges, and the features of this system. OfCourse, containing over 60,000 courses from the top 50 universities in the US, is currently available for public access at http://fusion.hpl.hp.com/OfCourse/.

This work studies the problem of extracting articles from Web pages for better printing. Different from existing approaches of article extraction, Web printing poses several unique requirements: 1) Identifying just the boundary surrounding the text-body is not the ideal solution for article extraction. It is highly desirable to filter out some uninformative links and advertisements within this boundary. 2) It is necessary to identify paragraphs, which may not be readily separated as DOM nodes, for the purpose of better layout of the article. 3) Its performance should be independent of content domains, written languages, and Web page templates. Toward these goals we propose a novel method of article extraction using both DOM (Document Object Model) and visual features. The main components of our method include: 1) a text segment/paragraph identification algorithm based on line-breaking features, 2) a global optimization method, Maximum Scoring Subsequence, based on text segments for identifying the boundary of the article body, 3) an outlier elimination step based on left or right alignment of text segments with the article body. Our experiments showed the proposed method is effective in terms of precision and recall at the level of text segments.

Temporally-ordered activity sequences are popular in many real-world domains. This paper presents an LDA-style topic model for sequences of temporal activities that captures three features of such sequences: 1) the counts of unique activities, 2) the Markov transition dependence and 3) the absolute or relative timestamp on each activity. In modeling the first two features we propose the concept of global transition probability and distinguish it with local transition probability used in previous work. In modeling the third feature, we employ a continuous time distribution to depict the time range of latent topics. The combination of the global transition probability and the temporal information helps to refine the mixture distribution over topics for temporal sequence analysis. We present results on the data of system call traces, showing better next activity prediction and sequence clustering.

Web content analysis often has two sequential and separate steps: Web Classification to identify the target Web pages, and Web Information Extraction to extract the metadata contained in the target Web pages. This decoupled strategy is highly ineffective since the errors in Web classification will be propagated to Web information extraction and eventually accumulate to a high level. In this paper we study the mutual dependencies between these two steps and propose to combine them by using a model of Conditional Random Fields (CRFs). This model can be used to simultaneously recognize the target Web pages and extract the corresponding metadata. Systematic experiments in our project OfCourse for online course search show that this model significantly improves the F1 value for both of the two steps. We believe that our model can be easily generalized to many Web applications.

Recent years have witnessed an increased interest in transfer learning. Despite the vast amount of research performed in this field, there are remaining challenges in applying the knowledge learnt from multiple source domains to a target domain. First, data from multiple source domains can be semantically related, but have different distributions. It is not clear how to exploit the distribution differences among multiple source domains to boost the learning performance in a target domain. Second, many real-world applications demand this transfer learning to be performed in a distributed manner. To meet these challenges, we propose a consensus regularization framework for transfer learning from multiple source domains to a target domain. In this framework, a local classifier is trained by considering both local data available in a source domain and the prediction consensus with the classifiers from other source domains. In addition, the training algorithm can be implemented in a distributed manner, in which all the source-domains are treated as slave nodes and the target domain is used as the master node. To combine the training results from multiple source domains, it only needs share some statistical data rather than the full contents of their labeled data. This can modestly relieve the privacy concerns and avoid the need to upload all data to a central location. Finally, our experimental results show the effectiveness of our consensus regularization learning.

Abstract. Interface automata [deH01] have been introduced as an interface theory [deH01a] capable of functioning as a behavioral type system. Behavioral type systems describe dynamic properties of components and their compositions. Like traditional (data) type systems, behavioral type systems can be used to check compatibility of components. In this paper, we use interface automata to devise a behavioral type system for Ptolemy II, leveraging the contravariant and optimistic properties of interface automata to achieve behavioral subtyping and polymorphism. Ptolemy II is a software framework supporting concurrent component composition according to diverse models of computation. In this paper, we focus on representing the communication protocols used in component communication within the behavioral type system. In building this type system, we identify two key limitations in interface automata formalisms; we overcome these limitations with two extensions, transient states and projection automata. In addition to static type checking, we also propose to extend the use of interface automata to the on-line reflection of component states and to run-time type checking, which enable dynamic component creation, morphing application structure, and admission control. We discuss the trade-offs in the design of behavioral type systems.

Modern embedded computing systems tend to be heterogeneous in the sense of being composed of subsystems with very different characteristics, which communicate and interact in a variety of ways-synchronous or asynchronous, buffered or unbuffered, etc. Obviously, when designing such systems, a modeling language needs to reflect this heterogeneity. Today's modeling environments usually offer a variant of what we call amorphous heterogeneity to address this problem. This paper argues that modeling systems in this manner leads to unexpected and hard-to-analyze interactions between the communication mechanisms and proposes a more structured approach to heterogeneity, called hierarchical heterogeneity, to solve this problem. It proposes a model structure and semantic framework that support this form of heterogeneity, and discusses the issues arising from heterogeneous component interaction and the desire for component reuse. It introduces the notion of domain polymorphism as a way to address these issues.