Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: 2010

  1. A Mixture Model with Sharing for Lexical Semantics
    [Details] [PDF] [Slides]
    Joseph Reisinger and Raymond J. Mooney
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2010), 1173--1182, MIT, Massachusetts, USA, October 9--11 2010.
    We introduce tiered clustering, a mixture model capable of accounting for varying degrees of shared (context-independent) feature structure, and demonstrate its applicability to inferring distributed representations of word meaning. Common tasks in lexical semantics such as word relatedness or selectional preference can benefit from modeling such structure: Polysemous word usage is often governed by some common background metaphoric usage (e.g. the senses of line or run), and likewise modeling the selectional preference of verbs relies on identifying commonalities shared by their typical arguments. Tiered clustering can also be viewed as a form of soft feature selection, where features that do not contribute meaningfully to the clustering can be excluded. We demonstrate the applicability of tiered clustering, highlighting particular cases where modeling shared structure is beneficial and where it can be detrimental.
    ML ID: 252
  2. Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision
    [Details] [PDF]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), 543--551, Beijing, China, August 2010.
    We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states consisting of multiple potential logical meaning representations. It disambiguates the meaning of each sentence while simultaneously learning a semantic parser that maps sentences into logical form. Compared to a previous generative model for semantic alignment, it also supports full semantic parsing. Experimental results on the Robocup sportscasting corpora in both English and Korean indicate that our approach produces more accurate semantic alignments than existing methods and also produces competitive semantic parsers and improved language generators.
    ML ID: 251
  3. Learning to Predict Readability using Diverse Linguistic Features
    [Details] [PDF] [Slides]
    Rohit J. Kate, Xiaoqiang Luo, Siddharth Patwardhan, Martin Franz, Radu Florian, Raymond J. Mooney, Salim Roukos and Chris Welty
    In 23rd International Conference on Computational Linguistics (COLING 2010), 2010.
    In this paper we consider the problem of building a system to predict readability of natural-language documents. Our system is trained using diverse features based on syntax and language models which are generally indicative of readability. The experimental results on a dataset of documents from a mix of genres show that the predictions of the learned system are more accurate than the predictions of naive human judges when compared against the predictions of linguistically-trained expert human judges. The experiments also compare the performances of different learning algorithms and different types of feature sets when used for predicting readability
    ML ID: 250
  4. Cross-cutting Models of Distributional Lexical Semantics
    [Details] [PDF] [Slides]
    Joseph S. Reisinger
    June 2010. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
    In order to respond to increasing demand for natural language interfaces—and provide meaningful insight into user query intent—fast, scalable lexical semantic models with flexible representations are needed. Human concept organization is a rich epiphenomenon that has yet to be accounted for by a single coherent psychological framework: Concept generalization is captured by a mixture of prototype and exemplar models, and local taxonomic information is available through multiple overlapping organizational systems. Previous work in computational linguistics on extracting lexical semantic information from the Web does not provide adequate representational flexibility and hence fails to capture the full extent of human conceptual knowledge. In this proposal I will outline a family of probabilistic models capable of accounting for the rich organizational structure found in human language that can predict contextual variation, selectional preference and feature-saliency norms to a much higher degree of accuracy than previous approaches. These models account for cross-cutting structure of concept organization—i.e. the notion that humans make use of different categorization systems for different kinds of generalization tasks—and can be applied to Web-scale corpora. Using these models, natural language systems will be able to infer a more comprehensive semantic relations, in turn improving question answering, text classification, machine translation, and information retrieval.
    ML ID: 249
  5. Spherical Topic Models
    [Details] [PDF] [Slides]
    Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond J. Mooney
    In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), 2010.
    We introduce the Spherical Admixture Model (SAM), a Bayesian topic model for arbitrary L2 normalized data. SAM maintains the same hierarchical structure as Latent Dirichlet Allocation (LDA), but models documents as points on a high-dimensional spherical manifold, allowing a natural likelihood parameterization in terms of cosine distance. Furthermore, SAM can model word absence/presence at the document level, and unlike previous models can assign explicit negative weight to topic terms. Performance is evaluated empirically, both through human ratings of topic quality and through diverse classification tasks from natural language processing and computer vision. In these experiments, SAM consistently outperforms existing models.
    ML ID: 248
  6. Joint Entity and Relation Extraction using Card-Pyramid Parsing
    [Details] [PDF] [Slides]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL-2010), 203--212, Uppsala, Sweden, July 2010.
    Both entity and relation extraction can benefit from being performed jointly, allowing each task to correct the errors of the other. We present a new method for joint entity and relation extraction using a graph we call a "card-pyramid". This graph compactly encodes all possible entities and relations in a sentence, reducing the task of their joint extraction to jointly labeling its nodes. We give an efficient labeling algorithm that is analogous to parsing using dynamic programming. Experimental results show improved results for our joint extraction method compared to a pipelined approach.
    ML ID: 247
  7. Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques
    [Details] [PDF] [Slides]
    Ruifang Ge
    PhD Thesis, Department of Computer Science, University of Texas at Austin, Austin, TX, May 2010. 165 pages.
    Natural language understanding is a sub-field of natural language processing, which builds automated systems to understand natural language. It is such an ambitious task that it sometimes is referred to as an AI-complete problem, implying that its difficulty is equivalent to solving the central artificial intelligence problem -- making computers as intelligent as people. Despite its complexity, natural language understanding continues to be a fundamental problem in natural language processing in terms of its theoretical and empirical importance.

    In recent years, startling progress has been made at different levels of natural language processing tasks, which provides great opportunity for deeper natural language understanding. In this thesis, we focus on the task of semantic parsing, which maps a natural language sentence into a complete, formal meaning representation in a meaning representation language. We present two novel state-of-the-art learned syntax-based semantic parsers using statistical syntactic parsing techniques, motivated by the following two reasons. First, the syntax-based semantic parsing is theoretically well-founded in computational semantics. Second, adopting a syntax-based approach allows us to directly leverage the enormous progress made in statistical syntactic parsing.

    The first semantic parser, SCISSOR, adopts an integrated syntactic-semantic parsing approach, in which a statistical syntactic parser is augmented with semantic parameters to produce a semantically-augmented parse tree (SAPT). This integrated approach allows both syntactic and semantic information to be available during parsing time to obtain an accurate combined syntactic-semantic analysis. The performance of SCISSOR is further improved by using discriminative reranking for incorporating non-local features. The second semantic parser, SYNSEM, exploits an existing syntactic parser to produce disambiguated parse trees that drive the compositional semantic interpretation. This pipeline approach allows semantic parsing to conveniently leverage the most recent progress in statistical syntactic parsing.

    We report experimental results on two real applications: an interpreter for coaching instructions in robotic soccer and a natural-language database interface, showing that the improvement of SCISSOR and SYNSEM over other systems is mainly on long sentences, where the knowledge of syntax given in the form of annotated SAPTs or syntactic parses from an existing parser helps semantic composition. SYNSEM also significantly improves results with limited training data, and is shown to be robust to syntactic errors.

    ML ID: 246
  8. Online Max-Margin Weight Learning with Markov Logic Networks
    [Details] [PDF] [Slides]
    Tuyen N. Huynh and Raymond J. Mooney
    In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), 32--37, Atlanta, GA, July 2010.
    Most of the existing weight-learning algorithms for Markov Logic Networks (MLNs) use batch training which becomes computationally expensive and even infeasible for very large datasets since the training examples may not fit in main memory. To overcome this problem, previous work has used online learning algorithms to learn weights for MLNs. However, this prior work has only applied existing online algorithms, and there is no comprehensive study of online weight learning for MLNs. In this paper, we derive new online algorithms for structured prediction using the primaldual framework, apply them to learn weights forMLNs, and compare against existing online algorithms on two large, real-world datasets. The experimental results show that the new algorithms achieve better accuracy than existing methods.
    ML ID: 245
  9. Bayesian Abductive Logic Programs
    [Details] [PDF] [Slides]
    Sindhu Raghavan and Raymond Mooney
    In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), 82--87, Atlanta, GA, July 2010.
    In this paper, we introduce Bayesian Abductive Logic Programs (BALPs), a new formalism that integrates Bayesian Logic Programs (BLPs) and Abductive Logic Programming (ALP) for abductive reasoning. Like BLPs, BALPs also combine first-order logic and Bayesian networks. However, unlike BLPs that use logical deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for solving problems like plan/activity recognition and diagnosis that require abductive reasoning. First, we present the necessary enhancements to BLPs in order to support logical abduction. Next, we apply BALPs to the task of plan recognition and demonstrate its efficacy on two data sets. We also compare the performance of BALPs with several existing approaches for abduction.
    ML ID: 244
  10. Authorship Attribution Using Probabilistic Context-Free Grammars
    [Details] [PDF] [Slides]
    Sindhu Raghavan, Adriana Kovashka and Raymond Mooney
    In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL-2010), 38--42, 2010.
    In this paper, we present a novel approach for authorship attribution, the task of identifying the author of a document, using probabilistic context-free grammars. Our approach involves building a probabilistic context-free grammar for each author and using this grammar as a language model for classification. We evaluate the performance of our method on a wide range of datasets to demonstrate its efficacy.
    ML ID: 243
  11. Using Closed Captions as Supervision for Video Activity Recognition
    [Details] [PDF]
    Sonal Gupta, Raymond J. Mooney
    In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2010), 1083--1088, Atlanta, GA, July 2010.
    Recognizing activities in real-world videos is a difficult problem exacerbated by background clutter, changes in camera angle and zoom, and rapid camera movements. Large corpora of labeled videos can be used to train automated activity recognition systems, but this requires expensive human labor and time. This paper explores how closed captions that naturally accompany many videos can act as weak supervision that allows automatically collecting ‘labeled’ data for activity recognition. We show that such an approach can improve activity retrieval in soccer videos. Our system requires no manual labeling of video clips and needs minimal human supervision. We also present a novel caption classifier that uses additional linguistic information to determine whether a specific comment refers to an ongoing activity. We demonstrate that combining linguistic analysis and automatically trained activity recognizers can significantly improve the precision of video retrieval.
    ML ID: 242
  12. Multi-Prototype Vector-Space Models of Word Meaning
    [Details] [PDF] [Slides]
    Joseph Reisinger, Raymond J. Mooney
    In Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2010), 109-117, 2010.
    Current vector-space models of lexical semantics create a single “prototype” vector to represent the meaning of a word. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. This paper presents a method that uses clustering to produce multiple “sense-specific&rdquo vectors for each word. This approach provides a context-dependent vector representation of word meaning that naturally accommodates homonymy and polysemy. Experimental comparisons to human judgements of semantic similarity for both isolated words as well as words in sentential contexts demonstrate the superiority of this approach over both prototype and exemplar based vector-space models.
    ML ID: 241
  13. Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language
    [Details] [PDF]
    David L. Chen, Joohyun Kim, Raymond J. Mooney
    Journal of Artificial Intelligence Research, 37:397--435, 2010.
    We present a novel framework for learning to interpret and generate language using only perceptual context as supervision. We demonstrate its capabilities by developing a system that learns to sportscast simulated robot soccer games in both English and Korean without any language-specific prior knowledge. Training employs only ambiguous supervision consisting of a stream of descriptive textual comments and a sequence of events extracted from the simulation trace. The system simultaneously establishes correspondences between individual comments and the events that they describe while building a translation model that supports both parsing and generation. We also present a novel algorithm for learning which events are worth describing. Human evaluations of the generated commentaries indicate they are of reasonable quality and in some cases even on par with those produced by humans for our limited domain.
    ML ID: 240