Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 2005

  1. Creating Diverse Ensemble Classifiers to Reduce Supervision
    [Details] [PDF]
    Prem Melville
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, November 2005. 141 pages. Technical Report TR-05-49.
    Ensemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an important factor in determining its generalization error. In this thesis, we present a new method for generating ensembles, DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), that directly constructs diverse hypotheses using additional artificially-generated training examples. The technique is a simple, general meta-learner that can use any strong learner as a base classifier to build diverse committees. The diverse ensembles produced by DECORATE are very effective for reducing the amount of supervision required for building accurate models. The first task we demonstrate this on is classification given a fixed training set. Experimental results using decision-tree induction as a base learner demonstrate that our approach consistently achieves higher predictive accuracy than the base classifier, Bagging and Random Forests. Also, DECORATE attains higher accuracy than Boosting on small training sets, and achieves comparable performance on larger training sets. Additional experiments demonstrate DECORATE's resilience to imperfections in data, in the form of missing features, classification noise, and feature noise.
    DECORATE ensembles can also be used to reduce supervision through active learning, in which the learner selects the most informative examples from a pool of unlabeled examples, such that acquiring their labels will increase the accuracy of the classifier. Query by Committee is one effective approach to active learning in which disagreement within the ensemble of hypotheses is used to select examples for labeling. Query by Bagging and Query by Boosting are two practical implementations of this approach that use Bagging and Boosting respectively, to build the committees. For efficient active learning it is critical that the committee be made up of consistent hypotheses that are very different from each other. Since DECORATE explicitly builds such committees, it is well-suited for this task. We introduce a new algorithm, Active-DECORATE, which uses DECORATE committees to select good training examples. Experimental results demonstrate that Active-DECORATE typically requires labeling fewer examples to achieve the same accuracy as Query by Bagging and Query by Boosting. Apart from optimizing classification accuracy, in many applications, producing good class probability estimates is also important, e.g., in fraud detection, which has unequal misclassification costs. This thesis introduces a novel approach to active learning based on Active-DECORATE which uses Jensen-Shannon divergence (a similarity measure for probability distributions) to improve the selection of training examples for optimizing probability estimation. Comprehensive experimental results demonstrate the benefits of our approach.
    Unlike the active learning setting, in many learning problems the class labels for all instances are known, but feature values may be missing and can be acquired at a cost. For building accurate predictive models, acquiring complete information for all instances is often quite expensive, while acquiring information for a random subset of instances may not be optimal. We formalize the task of active feature-value acquisition, which tries to reduce the cost of achieving a desired model accuracy by identifying instances for which obtaining complete information is most informative. We present an approach, based on DECORATE, in which instances are selected for acquisition based on the current model's accuracy and its confidence in the prediction. Experimental results demonstrate that our approach can induce accurate models using substantially fewer feature-value acquisitions than random sampling.
    ML ID: 182
  2. A Kernel-based Approach to Learning Semantic Parsers
    [Details] [PDF] [Slides]
    Rohit J. Kate
    2005. Doctoral Dissertation Proposal, University of Texas at Austin.
    Semantic parsing involves deep semantic analysis that maps natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing user-friendly natural language interfaces to computing systems. Most of the research in natural language understanding, however, has mainly focused on shallow semantic analysis like case-role analysis or word sense disambiguation. The existing work in semantic parsing either lack the robustness of statistical methods or are applicable only to simple domains where semantic analysis is equivalent to filling a single semantic frame.

    In this proposal, we present a new approach to semantic parsing based on string-kernel-based classification. Our system takes natural language sentences paired with their formal meaning representations as training data. For every production in the formal language grammar, a Support-Vector Machine (SVM) classifier is trained using string similarity as the kernel. Each classifier then gives the probability of the production covering any given natural language string of words. These classifiers are further refined using EM-type iterations based on their performance on the training data. Meaning representations for novel natural language sentences are obtained by finding the most probable semantic parse using these classifiers. Our experiments on two real-world data sets that have deep meaning representations show that this approach compares favorably to other existing systems in terms of accuracy and coverage.

    For future work, we propose to extend this approach so that it will also exploit the knowledge of natural language syntax by using the existing syntactic parsers. We also intend to broaden the scope of application domains, for example, domains where the sentences are noisy as typical in speech, or domains where corpora available for training do not have natural language sentences aligned with their unique meaning representations. We aim to test our system on the task of complex relation extraction as well. Finally, we also plan to investigate ways to combine our semantic parser with some recently developed semantic parsers to form committees in order to get the best overall performance.
    ML ID: 181
  3. Learning for Semantic Parsing Using Statistical Machine Translation Techniques
    [Details] [PDF]
    Yuk Wah Wong
    2005. Doctoral Dissertation Proposal, University of Texas at Austin.
    Semantic parsing is the construction of a complete, formal, symbolic meaning representation of a sentence. While it is crucial to natural language understanding, the problem of semantic parsing has received relatively little attention from the machine learning community. Recent work on natural language understanding has mainly focused on shallow semantic analysis, such as word- sense disambiguation and semantic role labeling. Semantic parsing, on the other hand, involves deep semantic analysis in which word senses, semantic roles and other components are combined to produce useful meaning representations for a particular application domain (e.g. database query). Prior research in machine learning for semantic parsing is mainly based on inductive logic programming or deterministic parsing, which lack some of the robustness that characterizes statistical learning. Existing statistical approaches to semantic parsing, however, are mostly concerned with relatively simple application domains in which a meaning representation is no more than a single semantic frame.

    In this proposal, we present a novel statistical approach to semantic parsing, WASP, which can handle meaning representations with a nested structure. The WASP algorithm learns a semantic parser given a set of sentences annotated with their correct meaning representations. The parsing model is based on the synchronous context-free grammar, where each rule maps a natural-language substring to its meaning representation. The main innovation of the algorithm is its use of state-of-the-art statistical machine translation techniques. A statistical word alignment model is used for lexical acquisition, and the parsing model itself can be seen as an instance of a syntax-based translation model. In initial evaluation on several real-world data sets, we show that WASP performs favorably in terms of both accuracy and coverage compared to existing learning methods requiring similar amount of supervision, and shows better robustness to variations in task complexity and word order.

    In future work, we intend to pursue several directions in developing accurate semantic parsers for a variety of application domains. This will involve exploiting prior knowledge about the natural-language syntax and the application domain. We also plan to construct a syntax-aware word-based alignment model for lexical acquisition. Finally, we will generalize the learning algorithm to handle context-dependent sentences and accept noisy training data.
    ML ID: 180
  4. An Expected Utility Approach to Active Feature-value Acquisition
    [Details] [PDF]
    P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney
    In Proceedings of the International Conference on Data Mining, 745-748, Houston, TX, November 2005.
    In many classification tasks training data have missing feature values that can be acquired at a cost. For building accurate predictive models, acquiring all missing values is often prohibitively expensive or unnecessary, while acquiring a random subset of feature values may not be most effective. The goal of active feature-value acquisition is to incrementally select feature values that are most cost-effective for improving the model's accuracy. We present an approach that acquires feature values for inducing a classification model based on an estimation of the expected improvement in model accuracy per unit cost. Experimental results demonstrate that our approach consistently reduces the cost of producing a model of a desired accuracy compared to random feature acquisitions.
    ML ID: 179
  5. Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping
    [Details] [PDF]
    Mikhail Bilenko, Sugato Basu, and Mehran Sahami
    In Proceedings of the 5th International Conference on Data Mining (ICDM-2005), 58--65, Houston, TX, November 2005.
    The problem of record linkage focuses on determining whether two object descriptions refer to the same underlying entity. Addressing this problem effectively has many practical applications, e.g., elimination of duplicate records in databases and citation matching for scholarly articles. In this paper, we consider a new domain where the record linkage problem is manifested: Internet comparison shopping. We address the resulting linkage setting that requires learning a similarity function between record pairs from streaming data. The learned similarity function is subsequently used in clustering to determine which records are co-referent and should be linked. We present an online machine learning method for addressing this problem, where a composite similarity function based on a linear combination of basis functions is learned incrementally. We illustrate the efficacy of this approach on several real-world datasets from an Internet comparison shopping site, and show that our method is able to effectively learn various distance functions for product data with differing characteristics. We also provide experimental results that show the importance of considering multiple performance measures in record linkage evaluation.
    ML ID: 178
  6. Combining Bias and Variance Reduction Techniques for Regression
    [Details] [PDF]
    Yuk Lai Suen, Prem Melville and Raymond J. Mooney
    Technical Report UT-AI-TR-05-321, University of Texas at Austin, July 2005. www.cs.utexas.edu/~ml/publication.
    Gradient Boosting and bagging applied to regressors can reduce the error due to bias and variance respectively. Alternatively, Stochastic Gradient Boosting (SGB) and Iterated Bagging (IB) attempt to simultaneously reduce the contribution of both bias and variance to error. We provide an extensive empirical analysis of these methods, along with two alternate bias-variance reduction approaches --- bagging Gradient Boosting (BagGB) and bagging Stochastic Gradient Boosting (BagSGB). Experimental results demonstrate that SGB does not perform as well as IB or the alternate approaches. Furthermore, results show that, while BagGB and BagSGB perform competitively for low-bias learners, in general, Iterated Bagging is the most effective of these methods.
    ML ID: 178
  7. Alignments and String Similarity in Information Integration: A Random Field Approach
    [Details] [PDF]
    Mikhail Bilenko and Raymond J. Mooney
    In Proceedings of the 2005 Dagstuhl Seminar on Machine Learning for the Semantic Web, Dagstuhl, Germany, February 2005.
    Several problems central to information integration, such as ontology mapping and object matching, can be viewed as alignment tasks where the goal is to find an optimal correspondence between two structured objects and to compute the associated similarity score. The diversity of data sources and domains in the Semantic Web requires solutions to these problems to be highly adaptive, which can be achieved by employing probabilistic machine learning approaches. We present one such approach, Alignment Conditional Random Fields (ACRFs), a new framework for constructing and scoring sequence alignments using undirected graphical models. ACRFs allow incorporating arbitrary features into string edit distance computation, yielding a learnable string similarity function for use in tasks where approximate string matching is needed. We outline possible applications of ACRFs in information integration tasks and describe directions for future work.
    ML ID: 177
  8. A Shortest Path Dependency Kernel for Relation Extraction
    [Details] [PDF]
    R. C. Bunescu, and Raymond J. Mooney
    In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-05), 724-731, Vancouver, BC, October 2005.
    We present a novel approach to relation extraction, based on the observation that the information required to assert a relationship between two named entities in the same sentence is typically captured by the shortest path between the two entities in the dependency graph. Experiments on extracting top-level relations from the ACE (Automated Content Extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels.
    ML ID: 175
  9. Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments
    [Details] [PDF]
    Sugato Basu
    PhD Thesis, University of Texas at Austin, 2005.
    Clustering is one of the most common data mining tasks, used frequently for data categorization and analysis in both industry and academia. The focus of our research is on semi-supervised clustering, where we study how prior knowledge, gathered either from automated information sources or human supervision, can be incorporated into clustering algorithms. In this thesis, we present probabilistic models for semi-supervised clustering, develop algorithms based on these models and empirically validate their performances by extensive experiments on data sets from different domains, e.g., text analysis, hand-written character recognition, and bioinformatics.

    In many domains where clustering is applied, some prior knowledge is available either in the form of labeled data (specifying the category to which an instance belongs) or pairwise constraints on some of the instances (specifying whether two instances should be in same or different clusters). In this thesis, we first analyze effective methods of incorporating labeled supervision into prototype-based clustering algorithms, and propose two variants of the well-known KMeans algorithm that can improve their performance with limited labeled data.

    We then focus on the problem of semi-supervised clustering with constraints and show how this problem can be studied in the framework of a well-defined probabilistic generative model of a Hidden Markov Random Field. We derive an efficient KMeans-type iterative algorithm, HMRF-KMeans, for optimizing a semi-supervised clustering objective function defined on the HMRF model. We also give convergence guarantees of our algorithm for a large class of clustering distortion measures (e.g., squared Euclidean distance, KL divergence, and cosine distance).

    Finally, we develop an active learning algorithm for acquiring maximally informative pairwise constraints in an interactive query-driven framework, which to our knowledge is the first active learning algorithm for semi-supervised clustering with constraints.

    Other interesting problems of semi-supervised clustering that we discuss in this thesis include (1) semi-supervised graph-based clustering using kernels, (2) using prior knowledge to improve overlapping clustering of data, (3) integration of both constraint-based and distance-based semi-supervised clustering methods using the HMRF model, and (4) model selection techniques that use the available supervision to automatically select the right number of clusters.
    ML ID: 174
  10. Combining Bias and Variance Reduction Techniques for Regression
    [Details] [PDF]
    Y. L. Suen, P. Melville and Raymond J. Mooney
    In Proceedings of the 16th European Conference on Machine Learning, 741-749, Porto, Portugal, October 2005.
    Gradient Boosting and bagging applied to regressors can reduce the error due to bias and variance respectively. Alternatively, Stochastic Gradient Boosting (SGB) and Iterated Bagging (IB) attempt to simultaneously reduce the contribution of both bias and variance to error. We provide an extensive empirical analysis of these methods, along with two alternate bias-variance reduction approaches --- bagging Gradient Boosting (BagGB) and bagging Stochastic Gradient Boosting (BagSGB). Experimental results demonstrate that SGB does not perform as well as IB or the alternate approaches. Furthermore, results show that, while BagGB and BagSGB perform competitively for low-bias learners, in general, Iterated Bagging is the most effective of these methods.
    ML ID: 173
  11. Consolidating the Set of Known Human Protein-Protein Interactions in Preparation for Large-Scale Mapping of the Human Interactome
    [Details] [PDF]
    A.K. Ramani, R.C. Bunescu, Raymond J. Mooney and E.M. Marcotte
    Genome Biology, 6(5):r40, 2005.
    Background

    Extensive protein interaction maps are being constructed for yeast, worm, and fly to ask how the proteins organize into pathways and systems, but no such genome-wide interaction map yet exists for the set of human proteins. To prepare for studies in humans, we wished to establish tests for the accuracy of future interaction assays and to consolidate the known interactions among human proteins.

    Results

    We established two tests of the accuracy of human protein interaction datasets and measured the relative accuracy of the available data. We then developed and applied natural language processing and literature-mining algorithms to recover from Medline abstracts 6,580 interactions among 3,737 human proteins. A three-part algorithm was used: first, human protein names were identified in Medline abstracts using a discriminator based on conditional random fields, then interactions were identified by the co-occurrence of protein names across the set of Medline abstracts, filtering the interactions with a Bayesian classifier to enrich for legitimate physical interactions. These mined interactions were combined with existing interaction data to obtain a network of 31,609 interactions among 7,748 human proteins, accurate to the same degree as the existing datasets.

    Conclusion

    These interactions and the accuracy benchmarks will aid interpretation of current functional genomics data and provide a basis for determining the quality of future large-scale human protein interaction assays. Projecting from the approximately 15 interactions per protein in the best-sampled interaction set to the estimated 25,000 human genes implies more than 375,000 interactions in the complete human protein interaction network. This set therefore represents no more than 10% of the complete network.
    ML ID: 172
  12. A Statistical Semantic Parser that Integrates Syntax and Semantics
    [Details] [PDF]
    Ruifang Ge and Raymond J. Mooney
    In Proceedings of CoNLL-2005, Ann Arbor, Michigan, June 2005.
    We introduce a learning semantic parser, Scissor, that maps natural-language sentences to a detailed, formal, meaning-representation language. It first uses an integrated statistical parser to produce a semantically augmented parse tree, in which each non-terminal node has both a syntactic and a semantic label. A compositional-semantics procedure is then used to map the augmented parse tree into a final meaning representation. We evaluate the system in two domains, a natural-language database interface and an interpreter for coaching instructions in robotic soccer. We present experimental results demonstrating that Scissor produces more accurate semantic representations than several previous approaches.
    ML ID: 171
  13. Mining Knowledge from Text Using Information Extraction
    [Details] [PDF]
    Raymond J. Mooney and R. Bunescu
    SIGKDD Explorations (special issue on Text Mining and Natural Language Processing), 7(1):3-10, 2005.
    An important approach to text mining involves the use of natural-language information extraction. Information extraction (IE) distills structured data or knowledge from unstructured text by identifying references to named entities as well as stated relationships between such entities. IE systems can be used to directly extricate abstract knowledge from a text corpus, or to extract concrete data from a set of documents which can then be further analyzed with traditional data-mining techniques to discover more general patterns. We discuss methods and implemented systems for both of these approaches and summarize results on mining real text corpora of biomedical abstracts, job announcements, and product descriptions. We also discuss challenges that arise when employing current information extraction technology to discover knowledge in text.
    ML ID: 170
  14. Economical Active Feature-value Acquisition through Expected Utility Estimation
    [Details] [PDF]
    P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney
    In Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, 10-16, Chicago, IL, August 2005.
    In many classification tasks training data have missing feature values that can be acquired at a cost. For building accurate predictive models, acquiring all missing values is often prohibitively expensive or unnecessary, while acquiring a random subset of feature values may not be most effective. The goal of Active Feature-value Acquisition is to incrementally select feature values that are most cost-effective for improving the model's accuracy. We present two policies, Sampled Expected Utility and Expected Utility-ES, that acquire feature values for inducing a classification model based on an estimation of the expected improvement in model accuracy per unit cost. A comparison of the two policies to each other and to alternative policies demonstrate that Sampled Expected Utility is preferable as it effectively reduces the cost of producing a model of a desired accuracy and exhibits a consistent performance across domains.
    ML ID: 168
  15. Semi-supervised Graph Clustering: A Kernel Approach
    [Details] [PDF]
    B. Kulis, S. Basu, I. Dhillon and Raymond J. Mooney
    In Proceedings of the 22nd International Conference on Machine Learning, 457--464, Bonn, Germany, August 2005. (Distinguished Student Paper Award).
    Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most of the semi-supervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vector-based and graph-based approaches. We show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel k-means objective. A recent theoretical connection between kernel k-means and several graph clustering objectives enables us to perform semi-supervised clustering of data given either as vectors or as a graph. For vector data, the kernel approach also enables us to find clusters with non-linear boundaries in the input data space. Furthermore, we show that recent work on spectral learning may be viewed as a special case of our formulation. We empirically show that our algorithm is able to outperform current state-of-the-art semi-supervised algorithms on both vector-based and graph-based data sets.
    ML ID: 167
  16. Using Biomedical Literature Mining to Consolidate the Set of Known Human Protein-Protein Interactions
    [Details] [PDF]
    A. Ramani, E. Marcotte, R. Bunescu and Raymond J. Mooney
    In Proceedings of the ISMB/ACL-05 Workshop of the BioLINK SIG: Linking Literature, Information and Knowledge for Biology, Detroit, MI, June 2005.
    This paper presents the results of a large-scale effort to construct a comprehensive database of known human protein interactions by combining and linking known interactions from existing databases and then adding to them by automatically mining additional interactions from 750,000 Medline abstracts. The end result is a network of 31,609 interactions amongst 7,748 proteins. The text mining system first identifies protein names in the text using a trained Conditional Random Field (CRF) and then identifies interactions through a filtered co-citation analysis. We also report two new strategies for mining interactions, either by finding explicit statements of interactions in the text using learned pattern-based rules or a Support-Vector Machine using a string kernel. Using information in existing ontologies, the automatically extracted data is shown to be of equivalent accuracy to manually curated data sets.
    ML ID: 164
  17. Model-based Overlapping Clustering
    [Details] [PDF]
    A. Banerjee, C. Krumpelman, S. Basu, Raymond J. Mooney and Joydeep Ghosh
    In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-05), 2005.
    While the vast majority of clustering algorithms are partitional, many real world datasets have inherently overlapping clusters. The recent explosion of analysis on biological datasets, which are frequently overlapping, has led to new clustering models that allow hard assignment of data points to multiple clusters. One particularly appealing model was proposed by Segal et al. in the context of probabilistic relational models (PRMs) applied to the analysis of gene microarray data. In this paper, we start with the basic approach of Segal et al. and provide an alternative interpretation of the model as a generalization of mixture models, which makes it easily interpretable. While the original model maximized likelihood over constant variance Gaussians, we generalize it to work with any regular exponential family distribution, and corresponding Bregman divergences, thereby making the model applicable for a wide variety of clustering distance functions, e.g., KL-divergence, Itakura-Saito distance, I-divergence. The general model is applicable to several domains, including high-dimensional sparse domains, such as text and recommender systems. We additionally offer several algorithmic modifications that improve both the performance and applicability of the model. We demonstrate the effectiveness of our algorithm through experiments on synthetic data as well as subsets of 20-Newsgroups and EachMovie datasets.
    ML ID: 163
  18. Towards Self-Configuring Hardware for Distributed Computer Systems
    [Details] [PDF]
    Jonathan Wildstrom, Peter Stone, E. Witchel, Raymond Mooney and M. Dahlin
    In The Second International Conference on Autonomic Computing, 241-249, June 2005.
    High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems online to optimize for dynamically changing workloads. This paper presents the initial steps towards a system that can learn to alter its current configuration in reaction to the current workload. In particular, the advantages of shifting CPU and memory resources online are considered. Investigation on a publically available multi-machine, multi-process distributed system (the online transaction processing benchmark TPC-W) indicates that there is a real performance benefit to reconfiguration in reaction to workload changes. A learning framework is presented that does not require any instrumentation of the middleware, nor any special instrumentation of the operating system; rather, it learns to identify preferable configurations as well as their quantitative performance effects from system behavior as reported by standard monitoring tools. Initial results using the WEKA machine learning package suggest that automatic adaptive configuration can provide measurable performance benefits over any fixed configuration.
    ML ID: 162
  19. Active Learning for Probability Estimation using Jensen-Shannon Divergence
    [Details] [PDF]
    P. Melville, S. M. Yang, M. Saar-Tsechansky and Raymond J. Mooney
    In Proceedings of the 16th European Conference on Machine Learning, 268--279, Porto, Portugal, October 2005.
    Active selection of good training examples is an important approach to reducing data-collection costs in machine learning; however, most existing methods focus on maximizing classification accuracy. In many applications, such as those with unequal misclassification costs, producing good class probability estimates (CPEs) is more important than optimizing classification accuracy. We introduce novel variations of two extant active-learning algorithms, Boostrap-LV and ACTIVEDECORATE, by using Jensen-Shannon divergence (a similarity measure for probability distributions) to improve sample selection for optimizing CPEs. Comprehensive experimental results demonstrate the benefits of our enhancements.
    ML ID: 161
  20. Learning to Transform Natural to Formal Languages
    [Details] [PDF] [Slides]
    Rohit J. Kate, Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), 1062-1068, Pittsburgh, PA, July 2005.
    This paper presents a method for inducing transformation rules that map natural-language sentences into a formal query or command language. The approach assumes a formal grammar for the target representation language and learns transformation rules that exploit the non-terminal symbols in this grammar. The learned transformation rules incrementally map a natural-language sentence or its syntactic parse tree into a parse-tree for the target formal language. Experimental results are presented for two corpora, one which maps English instructions into an existing formal coaching language for simulated RoboCup soccer agents, and another which maps English U.S.-geography questions into a database query language. We show that our method performs overall better and faster than previous approaches in both domains.
    ML ID: 160
  21. Explaining Recommendations: Satisfaction vs. Promotion
    [Details] [PDF]
    Mustafa Bilgic and Raymond J. Mooney
    In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, January 2005.
    Recommender systems have become a popular technique for helping users select desirable books, movies, music and other items. Most research in the area has focused on developing and evaluating algorithms for efficiently producing accurate recommendations. However, the ability to effectively explain its recommendations to users is another important aspect of a recommender system. The only previous investigation of methods for explaining recommendations showed that certain styles of explanations were effective at convincing users to adopt recommendations (i.e. promotion) but failed to show that explanations actually helped users make more accurate decisions (i.e. satisfaction). We present two new methods for explaining recommendations of content-based and/or collaborative systems and experimentally show that they actually improve user's estimation of item quality.
    ML ID: 156
  22. Learning for Collective Information Extraction
    [Details] [PDF]
    Razvan C. Bunescu
    Technical Report TR-05-02, Department of Computer Sciences, University of Texas at Austin, October 2005. Ph.D. proposal.
    An Information Extraction (IE) system analyses a set of documents with the aim of identifying certain types of entities and relations between them. Most IE systems treat separate potential extractions as independent. However, in many cases, considering influences between different candidate extractions could improve overall accuracy. For example, phrase repetitions inside a document are usually associated with the same entity type, the same being true for acronyms and their corresponding long form. One of our goals in this thesis is to show how these and potentially other types of correlations can be captured by a particular type of undirected probabilistic graphical model. Inference and learning using this graphical model allows for collective information extraction in a way that exploits the mutual influence between possible extractions. Preliminary experiments on learning to extract named entities from biomedical and newspaper text demonstrate the advantages of our approach.
    The benefit of doing collective classification comes however at a cost: in the general case, exact inference in the resulting graphical model has an exponential time complexity. The standard solution, which is also the one that we used in our initial work, is to resort to approximate inference. In this proposal we show that by considering only a selected subset of mutual influences between candidate extractions, exact inference can be done in linear time. Consequently, a short term goal is to run comparative experiments that would help us choose between the two approaches: exact inference with a restricted subset of mutual influences or approximate inference with the full set of influences.
    The set of issues that we intend to investigate in future work is two fold. One direction refers to applying the already developed framework to other natural language tasks that may benefit from the same types of influences, such as word sense disambiguation and part-of-speech tagging. Another direction concerns the design of a sufficiently general framework that would allow a seamless integration of cues from a variety of knowledge sources. We contemplate using generic sources such as external dictionaries, or web statistics on discriminative textual patterns. We also intend to alleviate the modeling problems due to the intrinsic local nature of entity features by exploiting syntactic information. All these generic features will be input to a feature selection algorithm, so that in the end we obtain a model which is both compact and accurate.
    ML ID: 155
  23. Comparative Experiments on Learning Information Extractors for Proteins and their Interactions
    [Details] [PDF]
    Razvan Bunescu, Ruifang Ge, Rohit J. Kate, Edward M. Marcotte, Raymond J. Mooney, Arun Kumar Ramani, and Yuk Wah Wong
    Artificial Intelligence in Medicine (special issue on Summarization and Information Extraction from Medical Documents)(2):139-155, 2005.
    Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data relevant to genes of the human genome from the 11 million abstracts in Medline. However, extraction efforts have been frustrated by the lack of conventions for describing human genes and proteins. We have developed and evaluated a variety of learned information extraction systems for identifying human protein names in Medline abstracts and subsequently extracting information on interactions between the proteins. We demonstrate that machine learning approaches using support vector machines and hidden Markov models are able to identify human proteins with higher accuracy than several previous approaches. We also demonstrate that various rule induction methods are able to identify protein interactions more accurately than manually-developed rules.
    ML ID: 137