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cs/0005013
Practical Reasoning for Very Expressive Description Logics
cs.LO cs.AI
Description Logics (DLs) are a family of knowledge representation formalisms mainly characterised by constructors to build complex concepts and roles from atomic ones. Expressive role constructors are important in many applications, but can be computationally problematical. We present an algorithm that decides satisf...
cs/0005014
Practical Reasoning for Expressive Description Logics
cs.LO cs.AI
Description Logics (DLs) are a family of knowledge representation formalisms mainly characterised by constructors to build complex concepts and roles from atomic ones. Expressive role constructors are important in many applications, but can be computationally problematical. We present an algorithm that decides satisf...
cs/0005015
Noun Phrase Recognition by System Combination
cs.CL
The performance of machine learning algorithms can be improved by combining the output of different systems. In this paper we apply this idea to the recognition of noun phrases.We generate different classifiers by using different representations of the data. By combining the results with voting techniques described i...
cs/0005016
Improving Testsuites via Instrumentation
cs.CL
This paper explores the usefulness of a technique from software engineering, namely code instrumentation, for the development of large-scale natural language grammars. Information about the usage of grammar rules in test sentences is used to detect untested rules, redundant test sentences, and likely causes of overge...
cs/0005017
Reasoning with Individuals for the Description Logic SHIQ
cs.LO cs.AI
While there has been a great deal of work on the development of reasoning algorithms for expressive description logics, in most cases only Tbox reasoning is considered. In this paper we present an algorithm for combined Tbox and Abox reasoning in the SHIQ description logic. This algorithm is of particular interest as...
cs/0005019
On the Scalability of the Answer Extraction System "ExtrAns"
cs.CL
This paper reports on the scalability of the answer extraction system ExtrAns. An answer extraction system locates the exact phrases in the documents that contain the explicit answers to the user queries. Answer extraction systems are therefore more convenient than document retrieval systems in situations where the u...
cs/0005020
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies
cs.CL cs.AI cs.DL cs.HC cs.IR
We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also describe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summari...
cs/0005021
Modeling the Uncertainty in Complex Engineering Systems
cs.AI cs.LG
Existing procedures for model validation have been deemed inadequate for many engineering systems. The reason of this inadequacy is due to the high degree of complexity of the mechanisms that govern these systems. It is proposed in this paper to shift the attention from modeling the engineering system itself to model...
cs/0005024
The SAT Phase Transition
cs.AI cs.CC
Phase transition is an important feature of SAT problem. For random k-SAT model, it is proved that as r (ratio of clauses to variables) increases, the structure of solutions will undergo a sudden change like satisfiability phase transition when r reaches a threshold point. This phenomenon shows that the satisfying tr...
cs/0005025
Finite-State Reduplication in One-Level Prosodic Morphology
cs.CL
Reduplication, a central instance of prosodic morphology, is particularly challenging for state-of-the-art computational morphology, since it involves copying of some part of a phonological string. In this paper I advocate a finite-state method that combines enriched lexical representations via intersection to implem...
cs/0005026
A One-Time Pad based Cipher for Data Protection in Distributed Environments
cs.CR cs.DC cs.IR cs.NI
A one-time pad (OTP) based cipher to insure both data protection and integrity when mobile code arrives to a remote host is presented. Data protection is required when a mobile agent could retrieve confidential information that would be encrypted in untrusted nodes of the network; in this case, information management...
cs/0005027
A Bayesian Reflection on Surfaces
cs.CV cs.DS cs.LG math.PR nlin.AO physics.data-an
The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finit...
cs/0005028
A method for command identification, using modified collision free hashing with addition & rotation iterative hash functions (part 1)
cs.HC cs.IR
This paper proposes a method for identification of a user`s fixed string set (which can be a command/instruction set for a terminal or microprocessor). This method is fast and has very small memory requirements, compared to a traditional full string storage and compare method. The user feeds characters into a microco...
cs/0005029
Ranking suspected answers to natural language questions using predictive annotation
cs.CL
In this paper, we describe a system to rank suspected answers to natural language questions. We process both corpus and query using a new technique, predictive annotation, which augments phrases in texts with labels anticipating their being targets of certain kinds of questions. Given a natural language question, an ...
cs/0005030
Axiomatizing Causal Reasoning
cs.AI cs.LO
Causal models defined in terms of a collection of equations, as defined by Pearl, are axiomatized here. Axiomatizations are provided for three successively more general classes of causal models: (1) the class of recursive theories (those without feedback), (2) the class of theories where the solutions to the equation...
cs/0005031
Conditional Plausibility Measures and Bayesian Networks
cs.AI
A general notion of algebraic conditional plausibility measures is defined. Probability measures, ranking functions, possibility measures, and (under the appropriate definitions) sets of probability measures can all be viewed as defining algebraic conditional plausibility measures. It is shown that algebraic conditio...
cs/0006001
Boosting the Differences: A fast Bayesian classifier neural network
cs.CV
A Bayesian classifier that up-weights the differences in the attribute values is discussed. Using four popular datasets from the UCI repository, some interesting features of the network are illustrated. The network is suitable for classification problems.
cs/0006002
Distorted English Alphabet Identification : An application of Difference Boosting Algorithm
cs.CV
The difference-boosting algorithm is used on letters dataset from the UCI repository to classify distorted raster images of English alphabets. In contrast to rather complex networks, the difference-boosting is found to produce comparable or better classification efficiency on this complex problem.
cs/0006003
Exploiting Diversity in Natural Language Processing: Combining Parsers
cs.CL
Three state-of-the-art statistical parsers are combined to produce more accurate parses, as well as new bounds on achievable Treebank parsing accuracy. Two general approaches are presented and two combination techniques are described for each approach. Both parametric and non-parametric models are explored. The resul...
cs/0006005
Novelty Detection for Robot Neotaxis
cs.RO cs.NE nlin.AO
The ability of a robot to detect and respond to changes in its environment is potentially very useful, as it draws attention to new and potentially important features. We describe an algorithm for learning to filter out previously experienced stimuli to allow further concentration on novel features. The algorithm use...
cs/0006006
A Real-Time Novelty Detector for a Mobile Robot
cs.RO cs.NE
Recognising new or unusual features of an environment is an ability which is potentially very useful to a robot. This paper demonstrates an algorithm which achieves this task by learning an internal representation of `normality' from sonar scans taken as a robot explores the environment. This model of the environment...
cs/0006007
Novelty Detection on a Mobile Robot Using Habituation
cs.RO cs.NE nlin.AO
In this paper a novelty filter is introduced which allows a robot operating in an un structured environment to produce a self-organised model of its surroundings and to detect deviations from the learned model. The environment is perceived using the rob ot's 16 sonar sensors. The algorithm produces a novelty measure ...
cs/0006009
Knowledge and common knowledge in a distributed environment
cs.DC cs.AI
Reasoning about knowledge seems to play a fundamental role in distributed systems. Indeed, such reasoning is a central part of the informal intuitive arguments used in the design of distributed protocols. Communication in a distributed system can be viewed as the act of transforming the system's state of knowledge. T...
cs/0006011
Bagging and Boosting a Treebank Parser
cs.CL
Bagging and boosting, two effective machine learning techniques, are applied to natural language parsing. Experiments using these techniques with a trainable statistical parser are described. The best resulting system provides roughly as large of a gain in F-measure as doubling the corpus size. Error analysis of the ...
cs/0006012
Exploiting Diversity for Natural Language Parsing
cs.CL
The popularity of applying machine learning methods to computational linguistics problems has produced a large supply of trainable natural language processing systems. Most problems of interest have an array of off-the-shelf products or downloadable code implementing solutions using various techniques. Where these so...
cs/0006013
An evaluation of Naive Bayesian anti-spam filtering
cs.CL cs.AI
It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail ("spam"). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate the effect of attribute-set size...
cs/0006017
Turning Speech Into Scripts
cs.CL
We describe an architecture for implementing spoken natural language dialogue interfaces to semi-autonomous systems, in which the central idea is to transform the input speech signal through successive levels of representation corresponding roughly to linguistic knowledge, dialogue knowledge, and domain knowledge. Th...
cs/0006018
Accuracy, Coverage, and Speed: What Do They Mean to Users?
cs.CL cs.HC
Speech is becoming increasingly popular as an interface modality, especially in hands- and eyes-busy situations where the use of a keyboard or mouse is difficult. However, despite the fact that many have hailed speech as being inherently usable (since everyone already knows how to talk), most users of speech input ar...
cs/0006019
A Compact Architecture for Dialogue Management Based on Scripts and Meta-Outputs
cs.CL
We describe an architecture for spoken dialogue interfaces to semi-autonomous systems that transforms speech signals through successive representations of linguistic, dialogue, and domain knowledge. Each step produces an output, and a meta-output describing the transformation, with an executable program in a simple s...
cs/0006020
A Comparison of the XTAG and CLE Grammars for English
cs.CL
When people develop something intended as a large broad-coverage grammar, they usually have a more specific goal in mind. Sometimes this goal is covering a corpus; sometimes the developers have theoretical ideas they wish to investigate; most often, work is driven by a combination of these two main types of goal. Wha...
cs/0006021
Compiling Language Models from a Linguistically Motivated Unification Grammar
cs.CL
Systems now exist which are able to compile unification grammars into language models that can be included in a speech recognizer, but it is so far unclear whether non-trivial linguistically principled grammars can be used for this purpose. We describe a series of experiments which investigate the question empiricall...
cs/0006023
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
cs.CL
We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discour...
cs/0006024
Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech?
cs.CL
Identifying whether an utterance is a statement, question, greeting, and so forth is integral to effective automatic understanding of natural dialog. Little is known, however, about how such dialog acts (DAs) can be automatically classified in truly natural conversation. This study asks whether current approaches, wh...
cs/0006025
Entropy-based Pruning of Backoff Language Models
cs.CL
A criterion for pruning parameters from N-gram backoff language models is developed, based on the relative entropy between the original and the pruned model. It is shown that the relative entropy resulting from pruning a single N-gram can be computed exactly and efficiently for backoff models. The relative entropy me...
cs/0006027
Verbal Interactions in Virtual Worlds
cs.CL cs.HC
We first discuss respective advantages of language interaction in virtual worlds and of using 3D images in dialogue systems. Then, we describe an example of a verbal interaction system in virtual reality: Ulysse. Ulysse is a conversational agent that helps a user navigate in virtual worlds. It has been designed to be...
cs/0006028
Trainable Methods for Surface Natural Language Generation
cs.CL
We present three systems for surface natural language generation that are trainable from annotated corpora. The first two systems, called NLG1 and NLG2, require a corpus marked only with domain-specific semantic attributes, while the last system, called NLG3, requires a corpus marked with both semantic attributes and...
cs/0006030
Multiagent Control of Self-reconfigurable Robots
cs.RO cs.DC cs.MA
We demonstrate how multiagent systems provide useful control techniques for modular self-reconfigurable (metamorphic) robots. Such robots consist of many modules that can move relative to each other, thereby changing the overall shape of the robot to suit different tasks. Multiagent control is particularly well-suite...
cs/0006031
Verifying Termination of General Logic Programs with Concrete Queries
cs.AI cs.LO
We introduce a method of verifying termination of logic programs with respect to concrete queries (instead of abstract query patterns). A necessary and sufficient condition is established and an algorithm for automatic verification is developed. In contrast to existing query pattern-based approaches, our method has t...
cs/0006032
Estimation of English and non-English Language Use on the WWW
cs.CL cs.HC
The World Wide Web has grown so big, in such an anarchic fashion, that it is difficult to describe. One of the evident intrinsic characteristics of the World Wide Web is its multilinguality. Here, we present a technique for estimating the size of a language-specific corpus given the frequency of commonly occurring wo...
cs/0006036
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
cs.CL
A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are absent in spoken langu...
cs/0006038
Approximation and Exactness in Finite State Optimality Theory
cs.CL
Previous work (Frank and Satta 1998; Karttunen, 1998) has shown that Optimality Theory with gradient constraints generally is not finite state. A new finite-state treatment of gradient constraints is presented which improves upon the approximation of Karttunen (1998). The method turns out to be exact, and very compac...
cs/0006039
Orthogonal Least Squares Algorithm for the Approximation of a Map and its Derivatives with a RBF Network
cs.NE cs.SD
Radial Basis Function Networks (RBFNs) are used primarily to solve curve-fitting problems and for non-linear system modeling. Several algorithms are known for the approximation of a non-linear curve from a sparse data set by means of RBFNs. However, there are no procedures that permit to define constrains on the deri...
cs/0006040
Correlation over Decomposed Signals: A Non-Linear Approach to Fast and Effective Sequences Comparison
cs.CV cs.DS q-bio
A novel non-linear approach to fast and effective comparison of sequences is presented, compared to the traditional cross-correlation operator, and illustrated with respect to DNA sequences.
cs/0006041
Using a Diathesis Model for Semantic Parsing
cs.CL cs.AI
This paper presents a semantic parsing approach for unrestricted texts. Semantic parsing is one of the major bottlenecks of Natural Language Understanding (NLU) systems and usually requires building expensive resources not easily portable to other domains. Our approach obtains a case-role analysis, in which the seman...
cs/0006042
Semantic Parsing based on Verbal Subcategorization
cs.CL cs.AI
The aim of this work is to explore new methodologies on Semantic Parsing for unrestricted texts. Our approach follows the current trends in Information Extraction (IE) and is based on the application of a verbal subcategorization lexicon (LEXPIR) by means of complex pattern recognition techniques. LEXPIR is framed on...
cs/0006043
Constraint compiling into rules formalism constraint compiling into rules formalism for dynamic CSPs computing
cs.AI
In this paper we present a rule based formalism for filtering variables domains of constraints. This formalism is well adapted for solving dynamic CSP. We take diagnosis as an instance problem to illustrate the use of these rules. A diagnosis problem is seen like finding all the minimal sets of constraints to be rela...
cs/0006044
Finite-State Non-Concatenative Morphotactics
cs.CL
Finite-state morphology in the general tradition of the Two-Level and Xerox implementations has proved very successful in the production of robust morphological analyzer-generators, including many large-scale commercial systems. However, it has long been recognized that these implementations have serious limitations ...
cs/0006047
Geometric Morphology of Granular Materials
cs.CV
We present a new method to transform the spectral pixel information of a micrograph into an affine geometric description, which allows us to analyze the morphology of granular materials. We use spectral and pulse-coupled neural network based segmentation techniques to generate blobs, and a newly developed algorithm t...
cs/0007001
Constraint Exploration and Envelope of Simulation Trajectories
cs.PL cs.AI cs.LO
The implicit theory that a simulation represents is precisely not in the individual choices but rather in the 'envelope' of possible trajectories - what is important is the shape of the whole envelope. Typically a huge amount of computation is required when experimenting with factors bearing on the dynamics of a simu...
cs/0007002
Interval Constraint Solving for Camera Control and Motion Planning
cs.AI cs.NA math.NA
Many problems in robust control and motion planning can be reduced to either find a sound approximation of the solution space determined by a set of nonlinear inequalities, or to the ``guaranteed tuning problem'' as defined by Jaulin and Walter, which amounts to finding a value for some tuning parameter such that a s...
cs/0007003
Using compression to identify acronyms in text
cs.DL cs.IR
Text mining is about looking for patterns in natural language text, and may be defined as the process of analyzing text to extract information from it for particular purposes. In previous work, we claimed that compression is a key technology for text mining, and backed this up with a study that showed how particular ...
cs/0007004
Brainstorm/J: a Java Framework for Intelligent Agents
cs.AI
Despite the effort of many researchers in the area of multi-agent systems (MAS) for designing and programming agents, a few years ago the research community began to take into account that common features among different MAS exists. Based on these common features, several tools have tackled the problem of agent devel...
cs/0007009
Incremental construction of minimal acyclic finite-state automata
cs.CL
In this paper, we describe a new method for constructing minimal, deterministic, acyclic finite-state automata from a set of strings. Traditional methods consist of two phases: the first to construct a trie, the second one to minimize it. Our approach is to construct a minimal automaton in a single phase by adding ne...
cs/0007010
Boosting Applied to Word Sense Disambiguation
cs.CL cs.AI
In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy on s...
cs/0007011
Naive Bayes and Exemplar-Based approaches to Word Sense Disambiguation Revisited
cs.CL cs.AI
This paper describes an experimental comparison between two standard supervised learning methods, namely Naive Bayes and Exemplar-based classification, on the Word Sense Disambiguation (WSD) problem. The aim of the work is twofold. Firstly, it attempts to contribute to clarify some confusing information about the com...
cs/0007012
Using Learning-based Filters to Detect Rule-based Filtering Obsolescence
cs.CL cs.AI
For years, Caisse des Depots et Consignations has produced information filtering applications. To be operational, these applications require high filtering performances which are achieved by using rule-based filters. With this technique, an administrator has to tune a set of rules for each topic. However, filters bec...
cs/0007013
Applying Constraint Handling Rules to HPSG
cs.CL cs.PL
Constraint Handling Rules (CHR) have provided a realistic solution to an over-arching problem in many fields that deal with constraint logic programming: how to combine recursive functions or relations with constraints while avoiding non-termination problems. This paper focuses on some other benefits that CHR, specif...
cs/0007016
Two Steps Feature Selection and Neural Network Classification for the TREC-8 Routing
cs.CL cs.AI
For the TREC-8 routing, one specific filter is built for each topic. Each filter is a classifier trained to recognize the documents that are relevant to the topic. When presented with a document, each classifier estimates the probability for the document to be relevant to the topic for which it has been trained. Sinc...
cs/0007017
Fuzzy data: XML may handle it
cs.IR
Data modeling is one of the most difficult tasks in application engineering. The engineer must be aware of the use cases and the required application services and at a certain point of time he has to fix the data model which forms the base for the application services. However, once the data model has been fixed it i...
cs/0007018
Bootstrapping a Tagged Corpus through Combination of Existing Heterogeneous Taggers
cs.CL
This paper describes a new method, Combi-bootstrap, to exploit existing taggers and lexical resources for the annotation of corpora with new tagsets. Combi-bootstrap uses existing resources as features for a second level machine learning module, that is trained to make the mapping to the new tagset on a very small sa...
cs/0007020
Polynomial-time Computation via Local Inference Relations
cs.LO cs.AI cs.PL
We consider the concept of a local set of inference rules. A local rule set can be automatically transformed into a rule set for which bottom-up evaluation terminates in polynomial time. The local-rule-set transformation gives polynomial-time evaluation strategies for a large variety of rule sets that cannot be given...
cs/0007022
ATLAS: A flexible and extensible architecture for linguistic annotation
cs.CL
We describe a formal model for annotating linguistic artifacts, from which we derive an application programming interface (API) to a suite of tools for manipulating these annotations. The abstract logical model provides for a range of storage formats and promotes the reuse of tools that interact through this API. We ...
cs/0007023
Towards a query language for annotation graphs
cs.CL cs.DB
The multidimensional, heterogeneous, and temporal nature of speech databases raises interesting challenges for representation and query. Recently, annotation graphs have been proposed as a general-purpose representational framework for speech databases. Typical queries on annotation graphs require path expressions si...
cs/0007024
Many uses, many annotations for large speech corpora: Switchboard and TDT as case studies
cs.CL
This paper discusses the challenges that arise when large speech corpora receive an ever-broadening range of diverse and distinct annotations. Two case studies of this process are presented: the Switchboard Corpus of telephone conversations and the TDT2 corpus of broadcast news. Switchboard has undergone two independ...
cs/0007026
Integrating E-Commerce and Data Mining: Architecture and Challenges
cs.LG cs.AI cs.CV cs.DB
We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce...
cs/0007031
Parameter-free Model of Rank Polysemantic Distribution
cs.CL
A model of rank polysemantic distribution with a minimal number of fitting parameters is offered. In an ideal case a parameter-free description of the dependence on the basis of one or several immediate features of the distribution is possible.
cs/0007032
Knowledge on Treelike Spaces
cs.LO cs.AI
This paper presents a bimodal logic for reasoning about knowledge during knowledge acquisition. One of the modalities represents (effort during) non-deterministic time and the other represents knowledge. The semantics of this logic are tree-like spaces which are a generalization of semantics used for modeling branchi...
cs/0007033
To Preference via Entrenchment
cs.LO cs.AI
We introduce a simple generalization of Gardenfors and Makinson's epistemic entrenchment called partial entrenchment. We show that preferential inference can be generated as the sceptical counterpart of an inference mechanism defined directly on partial entrenchment.
cs/0007035
Mapping WordNets Using Structural Information
cs.CL
We present a robust approach for linking already existing lexical/semantic hierarchies. We used a constraint satisfaction algorithm (relaxation labeling) to select --among a set of candidates-- the node in a target taxonomy that bests matches each node in a source taxonomy. In particular, we use it to map the nominal...
cs/0007036
Language identification of controlled systems: Modelling, control and anomaly detection
cs.CL
Formal language techniques have been used in the past to study autonomous dynamical systems. However, for controlled systems, new features are needed to distinguish between information generated by the system and input control. We show how the modelling framework for controlled dynamical systems leads naturally to a ...
cs/0007038
Modal Logics for Topological Spaces
cs.LO cs.AI
In this thesis we shall present two logical systems, MP and MP, for the purpose of reasoning about knowledge and effort. These logical systems will be interpreted in a spatial context and therefore, the abstract concepts of knowledge and effort will be defined by concrete mathematical concepts.
cs/0007039
Ordering-based Representations of Rational Inference
cs.LO cs.AI
Rational inference relations were introduced by Lehmann and Magidor as the ideal systems for drawing conclusions from a conditional base. However, there has been no simple characterization of these relations, other than its original representation by preferential models. In this paper, we shall characterize them with...
cs/0007040
Entrenchment Relations: A Uniform Approach to Nonmonotonicity
cs.LO cs.AI
We show that Gabbay's nonmonotonic consequence relations can be reduced to a new family of relations, called entrenchment relations. Entrenchment relations provide a direct generalization of epistemic entrenchment and expectation ordering introduced by Gardenfors and Makinson for the study of belief revision and expe...
cs/0007041
Relevance as Deduction: A Logical View of Information Retrieval
cs.IR cs.LO
The problem of Information Retrieval is, given a set of documents D and a query q, providing an algorithm for retrieving all documents in D relevant to q. However, retrieval should depend and be updated whenever the user is able to provide as an input a preferred set of relevant documents; this process is known as em...
cs/0007044
Managing Periodically Updated Data in Relational Databases: A Stochastic Modeling Approach
cs.DB
Recent trends in information management involve the periodic transcription of data onto secondary devices in a networked environment, and the proper scheduling of these transcriptions is critical for efficient data management. To assist in the scheduling process, we are interested in modeling the reduction of consist...
cs/0008003
Interfacing Constraint-Based Grammars and Generation Algorithms
cs.CL
Constraint-based grammars can, in principle, serve as the major linguistic knowledge source for both parsing and generation. Surface generation starts from input semantics representations that may vary across grammars. For many declarative grammars, the concept of derivation implicitly built in is that of parsing. Th...
cs/0008004
Comparing two trainable grammatical relations finders
cs.CL
Grammatical relationships (GRs) form an important level of natural language processing, but different sets of GRs are useful for different purposes. Therefore, one may often only have time to obtain a small training corpus with the desired GR annotations. On such a small training corpus, we compare two systems. They ...
cs/0008005
More accurate tests for the statistical significance of result differences
cs.CL
Statistical significance testing of differences in values of metrics like recall, precision and balanced F-score is a necessary part of empirical natural language processing. Unfortunately, we find in a set of experiments that many commonly used tests often underestimate the significance and so are less likely to det...
cs/0008007
Tagger Evaluation Given Hierarchical Tag Sets
cs.CL
We present methods for evaluating human and automatic taggers that extend current practice in three ways. First, we show how to evaluate taggers that assign multiple tags to each test instance, even if they do not assign probabilities. Second, we show how to accommodate a common property of manually constructed ``gol...
cs/0008008
On the Average Similarity Degree between Solutions of Random k-SAT and Random CSPs
cs.AI cs.CC cs.DM
To study the structure of solutions for random k-SAT and random CSPs, this paper introduces the concept of average similarity degree to characterize how solutions are similar to each other. It is proved that under certain conditions, as r (i.e. the ratio of constraints to variables) increases, the limit of average si...
cs/0008009
Data Mining to Measure and Improve the Success of Web Sites
cs.LG cs.DB
For many companies, competitiveness in e-commerce requires a successful presence on the web. Web sites are used to establish the company's image, to promote and sell goods and to provide customer support. The success of a web site affects and reflects directly the success of the company in the electronic market. In t...
cs/0008012
Applying System Combination to Base Noun Phrase Identification
cs.CL
We use seven machine learning algorithms for one task: identifying base noun phrases. The results have been processed by different system combination methods and all of these outperformed the best individual result. We have applied the seven learners with the best combinator, a majority vote of the top five systems, ...
cs/0008013
Meta-Learning for Phonemic Annotation of Corpora
cs.CL
We apply rule induction, classifier combination and meta-learning (stacked classifiers) to the problem of bootstrapping high accuracy automatic annotation of corpora with pronunciation information. The task we address in this paper consists of generating phonemic representations reflecting the Flemish and Dutch pronu...
cs/0008014
Aspects of Pattern-Matching in Data-Oriented Parsing
cs.CL
Data-Oriented Parsing (dop) ranks among the best parsing schemes, pairing state-of-the art parsing accuracy to the psycholinguistic insight that larger chunks of syntactic structures are relevant grammatical and probabilistic units. Parsing with the dop-model, however, seems to involve a lot of CPU cycles and a consi...
cs/0008015
Temiar Reduplication in One-Level Prosodic Morphology
cs.CL
Temiar reduplication is a difficult piece of prosodic morphology. This paper presents the first computational analysis of Temiar reduplication, using the novel finite-state approach of One-Level Prosodic Morphology originally developed by Walther (1999b, 2000). After reviewing both the data and the basic tenets of On...
cs/0008016
Processing Self Corrections in a speech to speech system
cs.CL cs.AI
Speech repairs occur often in spontaneous spoken dialogues. The ability to detect and correct those repairs is necessary for any spoken language system. We present a framework to detect and correct speech repairs where all relevant levels of information, i.e., acoustics, lexis, syntax and semantics can be integrated....
cs/0008017
Efficient probabilistic top-down and left-corner parsing
cs.CL
This paper examines efficient predictive broad-coverage parsing without dynamic programming. In contrast to bottom-up methods, depth-first top-down parsing produces partial parses that are fully connected trees spanning the entire left context, from which any kind of non-local dependency or partial semantic interpret...
cs/0008019
An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-mail Messages
cs.CL cs.IR cs.LG
The growing problem of unsolicited bulk e-mail, also known as "spam", has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is trai...
cs/0008020
Explaining away ambiguity: Learning verb selectional preference with Bayesian networks
cs.CL cs.AI
This paper presents a Bayesian model for unsupervised learning of verb selectional preferences. For each verb the model creates a Bayesian network whose architecture is determined by the lexical hierarchy of Wordnet and whose parameters are estimated from a list of verb-object pairs found from a corpus. ``Explaining ...
cs/0008021
Compact non-left-recursive grammars using the selective left-corner transform and factoring
cs.CL
The left-corner transform removes left-recursion from (probabilistic) context-free grammars and unification grammars, permitting simple top-down parsing techniques to be used. Unfortunately the grammars produced by the standard left-corner transform are usually much larger than the original. The selective left-corner...
cs/0008022
A Learning Approach to Shallow Parsing
cs.LG cs.CL
A SNoW based learning approach to shallow parsing tasks is presented and studied experimentally. The approach learns to identify syntactic patterns by combining simple predictors to produce a coherent inference. Two instantiations of this approach are studied and experimental results for Noun-Phrases (NP) and Subject...
cs/0008023
Selectional Restrictions in HPSG
cs.CL
Selectional restrictions are semantic sortal constraints imposed on the participants of linguistic constructions to capture contextually-dependent constraints on interpretation. Despite their limitations, selectional restrictions have proven very useful in natural language applications, where they have been used freq...
cs/0008024
Estimation of Stochastic Attribute-Value Grammars using an Informative Sample
cs.CL
We argue that some of the computational complexity associated with estimation of stochastic attribute-value grammars can be reduced by training upon an informative subset of the full training set. Results using the parsed Wall Street Journal corpus show that in some circumstances, it is possible to obtain better esti...
cs/0008026
Noun-phrase co-occurrence statistics for semi-automatic semantic lexicon construction
cs.CL
Generating semantic lexicons semi-automatically could be a great time saver, relative to creating them by hand. In this paper, we present an algorithm for extracting potential entries for a category from an on-line corpus, based upon a small set of exemplars. Our algorithm finds more correct terms and fewer incorrect...
cs/0008027
Measuring efficiency in high-accuracy, broad-coverage statistical parsing
cs.CL
Very little attention has been paid to the comparison of efficiency between high accuracy statistical parsers. This paper proposes one machine-independent metric that is general enough to allow comparisons across very different parsing architectures. This metric, which we call ``events considered'', measures the numb...
cs/0008028
Estimators for Stochastic ``Unification-Based'' Grammars
cs.CL
Log-linear models provide a statistically sound framework for Stochastic ``Unification-Based'' Grammars (SUBGs) and stochastic versions of other kinds of grammars. We describe two computationally-tractable ways of estimating the parameters of such grammars from a training corpus of syntactic analyses, and apply these...
cs/0008029
Exploiting auxiliary distributions in stochastic unification-based grammars
cs.CL
This paper describes a method for estimating conditional probability distributions over the parses of ``unification-based'' grammars which can utilize auxiliary distributions that are estimated by other means. We show how this can be used to incorporate information about lexical selectional preferences gathered from ...
cs/0008030
Metonymy Interpretation Using X NO Y Examples
cs.CL
We developed on example-based method of metonymy interpretation. One advantages of this method is that a hand-built database of metonymy is not necessary because it instead uses examples in the form ``Noun X no Noun Y (Noun Y of Noun X).'' Another advantage is that we will be able to interpret newly-coined metonymic ...
cs/0008031
Bunsetsu Identification Using Category-Exclusive Rules
cs.CL
This paper describes two new bunsetsu identification methods using supervised learning. Since Japanese syntactic analysis is usually done after bunsetsu identification, bunsetsu identification is important for analyzing Japanese sentences. In experiments comparing the four previously available machine-learning method...
cs/0008032
Japanese Probabilistic Information Retrieval Using Location and Category Information
cs.CL
Robertson's 2-poisson information retrieve model does not use location and category information. We constructed a framework using location and category information in a 2-poisson model. We submitted two systems based on this framework to the IREX contest, Japanese language information retrieval contest held in Japan ...