12982947
Support Vector Learning for Semantic Argument Classification
The natural language processing community has recently experienced a growth of interest in domain independent shallow semantic parsing—the process of assigning a Who did What to Whom, When, Where, Why, How etc. structure to plain text. This process entails identifying groups of words in a sentence that represent these semantic arguments and assigning specific labels to them. It could play a key role in NLP tasks like Information Extraction, Question Answering and Summarization. We propose a machine learning algorithm for semantic role parsing, extending the work of Gildea and Jurafsky (2002), Surdeanu et al. (2003) and others. Our algorithm is based on Support Vector Machines which we show give large improvement in performance over earlier classifiers. We show performance improvements through a number of new features designed to improve generalization to unseen data, such as automatic clustering of verbs. We also report on various analytic studies examining which features are most important, comparing our classifier to other machine learning algorithms in the literature, and testing its generalization to new test set from different genre. On the task of assigning semantic labels to the PropBank (Kingsbury, Palmer, & Marcus, 2002) corpus, our final system has a precision of 84% and a recall of 75%, which are the best results currently reported for this task. Finally, we explore a completely different architecture which does not requires a deep syntactic parse. We reformulate the task as a combined chunking and classification problem, thus allowing our algorithm to be applied to new languages or genres of text for which statistical syntactic parsers may not be available.
33984452
Using System Networks to Build Rhetorical Structures
We are currently engaged in a project to study the generation of instructional texts for common consumer devices. Our initial efforts have focused on an exhaustive corpus-based analysis of instruction booklets for cordless telephones. In this paper, we present our analysis of the way the various processes used in this domain are expressed. Our emphasis here is on the relationship between the surface grammatical coding of these processes and the underlying rhetorical structure of the text. This analysis has been formalized using a systemic-functional framework, with the resulting system networks forming the basis for the Imagene text generation system.
15685117
MetaBank: A Knowledge-Base of Metaphoric Language Conventions
The frequent and conventional use of nonliteral language has been a major stumbling block for natural language processing systems since the early machine translation efforts. Metaphor, metonymy, and indirect speech acts are among the most troublesome phenomena. Recent computational efforts addressing these problems have taken an approach that emphasizes the use of systematic knowledge about nonliteral language conventions. We are currently engaged in an effort to supply this knowledge in the case of conventional metaphor. We are constructing MetaBank: an empirically derived and theoretically motivated knowledge-base of English metaphorical conventions. This article describes our three-part approach to the construction of MetaBank: the collection of on-line textual resources and databases of linguistic generalizations, the development of a methodology for analyzing these resources, and the construction of a knowledge-base based on the preceding analyses.
61042466
The berkeley UNIX consultant project
UC (UNIX Consultant) is an intelligent, natural language interface that allows naive users to learn about the UNIX2 operating system. UC was undertaken because the task was thought to be both a fertile domain for artificial intelligence (AI) research and a useful application of AI work in planning, reasoning, natural language processing, and knowledge representation.The current implementation of UC comprises the following components: a language analyzer, called ALANA, produces a representation of the content contained in an utterance; an inference component, called a concretion mechanism, that further refines this content; a goal analyzer, PAGAN, that hypothesizes the plans and goals under which the user is operating; an agent, called UCEgo, that decides on UC's goals and proposes plans for them; a domain planner, called KIP, that computes a plan to address the user's request; an expression mechanism, UCExpress, that determines the content to be communicated to the user, and a language production mechanism, UCGen, that expresses UC's response in English.UC also contains a component, called KNOME, that builds a model of the user's knowledge state with respect to UNIX. Another mechanism, UCTeacher, allows a user to add knowledge of both English vocabulary and facts about UNIX to UC's knowledge base. This is done by interacting with the user in natural language.All these aspects of UC make use of knowledge represented in a knowledge representation system called KODIAK. KODIAK is a relation-oriented system that is intended to have wide representational range and a clear semantics, while maintaining a cognitive appeal. All of UC's knowledge, ranging from its most general concepts to the content of a particular utterance, is represented in KODIAK.
8592573
Contextual Spelling Correction Using Latent Semantic Analysis
Contextual spelling errors are defined as the use of an incorrect, though valid, word in a particular sentence or context. Traditional spelling checkers flag misspelled words, but they do not typically attempt to identify words that are used incorrectly in a sentence. We explore the use of Latent Semantic Analysis for correcting these incorrectly used words and the results are compared to earlier work based on a Bayesian classifier.
2946678
Conventional Metaphor and the Lexicon
Metaphor and other forms of non-literal language are essential parts of language which have direct bearing on theories of lexical semantics. Neither narrow theories of lexical semantics, nor theories relying solely on world knowledge are sufficient to account for our ability to generate and interpret non-literal language. This paper presents an emerging approach that may provide such an account. This approach is based on systematic representations that capture non-literal language conventions, and mechanisms that can dynamically understand and learn new uses as they are encountered.
5177142
Spectral techniques for graph bisection in genetic algorithms
Various applications of spectral techniques for enhancing graph bisection in genetic algorithms are investigated. Several enhancements to a genetic algorithm for graph bisection are introduced based on spectral decompositions of adjacency matrices of graphs and subpopulation matrices. First, the spectral decompositions give initial populations for the genetic algorithm to start with. Next, spectral techniques are used to engineer new individuals and reorder the schema to strategically group certain sets of vertices together on the chromosome. The operators and techniques are found to be beneficial when added to a plain genetic algorithm and when used in conjunction with other local optimization techniques for graph bisection. In addition, several world record minimum bisections have been obtained from the methods described in this study.
53305414
Fast Byzantine Consensus
We present the first protocol that reaches asynchronous Byzantine consensus in two communication steps in the common case. We prove that our protocol is optimal in terms of both number of communication steps and number of processes for two-step consensus. The protocol can be used to build a replicated state machine that requires only three communication steps per request in the common case. Further, we show a parameterized version of the protocol that is safe despite f Byzantine failures and, in the common case, guarantees two-step execution despite some number t of failures (t les f). We show that this parameterized two-step consensus protocol is also optimal in terms of both number of communication steps and number of processes
6534012
PreBIND and Textomy – mining the biomedical literature for protein-protein interactions using a support vector machine
BackgroundThe majority of experimentally verified molecular interaction and biological pathway data are present in the unstructured text of biomedical journal articles where they are inaccessible to computational methods. The Biomolecular interaction network database (BIND) seeks to capture these data in a machine-readable format. We hypothesized that the formidable task-size of backfilling the database could be reduced by using Support Vector Machine technology to first locate interaction information in the literature. We present an information extraction system that was designed to locate protein-protein interaction data in the literature and present these data to curators and the public for review and entry into BIND.ResultsCross-validation estimated the support vector machine's test-set precision, accuracy and recall for classifying abstracts describing interaction information was 92%, 90% and 92% respectively. We estimated that the system would be able to recall up to 60% of all non-high throughput interactions present in another yeast-protein interaction database. Finally, this system was applied to a real-world curation problem and its use was found to reduce the task duration by 70% thus saving 176 days.ConclusionsMachine learning methods are useful as tools to direct interaction and pathway database back-filling; however, this potential can only be realized if these techniques are coupled with human review and entry into a factual database such as BIND. The PreBIND system described here is available to the public at http://bind.ca. Current capabilities allow searching for human, mouse and yeast protein-interaction information.
14705463
C to Java migration experiences
With the growing popularity of the Java programming language for both client and server side applications in network-centric computing, there is a rising need for programming libraries that can be easily integrated into Java programs. In a previous paper, we surveyed current strategies for integrating C source code into Java programs, pointed out their weaknesses and presented goals for an improved migration approach. In this paper, we present the Ephedra approach to software migration and report on the results of three case studies transliterating C source code to Java using the Ephedra environment.
4484500
Perception of Blended Emotions: From Video Corpus to Expressive Agent
Real life emotions are often blended and involve several simultaneous superposed or masked emotions. This paper reports on a study on the perception of multimodal emotional behaviors in Embodied Conversational Agents. This experimental study aims at evaluating if people detect properly the signs of emotions in different modalities (speech, facial expressions, gestures) when they appear to be superposed or masked. We compared the perception of emotional behaviors annotated in a corpus of TV interviews and replayed by an expressive agent at different levels of abstraction. The results provide insights on the use of such protocols for studying the effect of various models and modalities on the perception of complex emotions.