6898492
Using Conditional Random Fields for Sentence Boundary Detection in Speech
Sentence boundary detection in speech is important for enriching speech recognition output, making it easier for humans to read and downstream modules to process. In previous work, we have developed hidden Markov model (HMM) and maximum entropy (Maxent) classifiers that integrate textual and prosodic knowledge sources for detecting sentence boundaries. In this paper, we evaluate the use of a conditional random field (CRF) for this task and relate results with this model to our prior work. We evaluate across two corpora (conversational telephone speech and broadcast news speech) on both human transcriptions and speech recognition output. In general, our CRF model yields a lower error rate than the HMM and Maxent models on the NIST sentence boundary detection task in speech, although it is interesting to note that the best results are achieved by three-way voting among the classifiers. This probably occurs because each model has different strengths and weaknesses for modeling the knowledge sources.
8135643
Joint Segmentation and Classification of Dialog Acts in Multiparty Meetings
This paper investigates a scheme for joint segmentation and classification of dialog acts (DAs) of the ICSI Meeting Corpus based on hidden-event language models and a maximum entropy classifier for the modeling of word boundary types. Specifically, the modeling of the boundary types takes into account dependencies between the duration of a pause and its surrounding words. Results for the proposed method compare favorably with our previous work on the same task
9822374
Initial Study on Automatic Identification of Speaker Role in Broadcast News Speech
Identifying a speaker's role (anchor, reporter, or guest speaker) is important for finding the structural information in broadcast news speech. We present an HMM-based approach and a maximum entropy model for speaker role labeling using Mandarin broadcast news speech. The algorithms achieve classification accuracy of about 80% (compared to the baseline of around 50%) using the human transcriptions and manually labeled speaker turns. We found that the maximum entropy model performs slightly better than the HMM, and that the combination of them outperforms any model alone. The impact of the contextual role information is also examined in this study.
37864683
Genetic programming model for software quality classification
We apply genetic programming techniques to build a software quality classification model based on the metrics of software modules. The model we built attempts to distinguish the fault-prone modules from non-fault-prone modules using genetic programming (GP). These GP experiments were conducted with a random subset selection for GP in order to avoid overfitting. We then use the whole fit data set as the validation data set to select the best model. We demonstrate through two case studies that the GP technique can achieve good results. Also, we compared GP modeling with logistic regression modeling to verify the usefulness of GP.
36530209
Determination of the star valency of a graph
The star valency of a graph G is the minimum, over all star decompositions π, of the maximum number of elements in π incident with a vertex. The maximum average degree of G, denoted by dmax-ave(G), is the maximum average degree of all subgraphs of G. In this paper, we prove that the star valency of G is either ⌈dmax-ave(G)/2⌉ or ⌈dmax-ave(G)/2⌉ + 1, and provide a polynomial time algorithm for determining the star valency of a graph.
2786167
Location-aware topology matching in P2P systems
Peer-to-peer (P2P) computing has emerged as a popular model aiming at further utilizing Internet information and resources, complementing the available client-server services. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatching between the P2P overlay network and the physical underlying network. The topology mismatching problem brings a great stress in the Internet infrastructure and greatly limits the performance gain from various search or routing techniques. Meanwhile, due to the inefficient overlay topology, the flooding-based search mechanisms cause a large volume of unnecessary traffic. Aiming at alleviating the mismatching problem and reducing the unnecessary traffic, we propose a location-aware topology matching (LTM) technique, an algorithm of building an efficient overlay by disconnecting low productive connections and choosing physically closer nodes as logical neighbors while still retaining the search scope and reducing response time for queries. LTM is scalable and completely distributed in the sense that it does not require any global knowledge of the whole overlay network when each node is optimizing the organization of its logical neighbors. The effectiveness of LTM is demonstrated through simulation studies.
3096549
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models is given which shows how kernel conditional random fields arise from risk minimization procedures defined using Mercer kernels on labeled graphs. A procedure for greedily selecting cliques in the dual representation is then proposed, which allows sparse representations. By incorporating kernels and implicit feature spaces into conditional graphical models, the framework enables semi-supervised learning algorithms for structured data through the use of graph kernels. The framework and clique selection methods are demonstrated in synthetic data experiments, and are also applied to the problem of protein secondary structure prediction.
12158249
Bisingular maps on the sphere and the projective plane
Abstract A map is bisingular if each edge is either a loop or an isthmus (i.e., on the boundary of the same face). In this paper we study the number of rooted bisingular maps on the plane or projective plane and also we present formulae for such maps with up to three or more parameters: the root valency, the number of isthmus, the number of essential loops, and the number of planar loops.