44345524
Extracting Keyphrases from Spoken Audio Documents
Spoken audio documents are becoming more and more common on the World Wide Web, and this is likely to be accelerated by the widespread deployment of broadband technologies. Unfortunately, speech documents are inherently hard to browse because of their transient nature. One approach to this problem is to label segments of a spoken document with keyphrases that summarise them. In this paper, we investigate an approach for automatically extracting keyphrases from spoken audio documents. We use a keyphrase extraction system (Extractor) originally developed for text, and apply it to errorful Speech Recognition transcripts, which may contain multiple hypotheses for each of the utterances. We show that keyphrase extraction is an "easier" task than full text transcription and that keyphrases can be extracted with reasonable precision from transcripts with Word Error Rates (WER) as high as 62%. This robustness to noise can be attributed to the fact that keyphrase words have a lower WER than non-keyphrase words and that they tend to have more redundancy in the audio. From this we conclude that keyphrase extraction is feasible for a wide range of spoken documents, including less-than-broadcast casual speech. We also show that including multiple utterance hypotheses does not improve the precision of the extracted keyphrases.
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.
5918485
A framework for dynamic Byzantine storage
We present a framework for transforming several quorum-based protocols so that they can dynamically adapt their failure threshold and server count, allowing them to be reconfigured in anticipation of possible failures or to replace servers as desired. We demonstrate this transformation on the dissemination quorum protocol. The resulting system provides confirmable wait-free atomic semantics while tolerating Byzantine failures from the clients or servers. The system can grow without bound to tolerate as many failures as desired. Finally, the protocol is optimal and fast: only the minimal number of servers - 3f + 1 - is needed to tolerate any f failures and, in the common case, reads require only one message round-trip.
618620
Corpus-based static branch prediction
Correctly predicting the direction that branches will take is increasingly important in today's wide-issue computer architectures. The name program-based branch prediction is given to static branch prediction techniques that base their prediction on a program's structure. In this paper, we investigate a new approach to program-based branch prediction that uses a body of existing programs to predict the branch behavior in a new program. We call this approach to program-based branch prediction, evidence-based static prediction, or ESP. The main idea of ESP is that the behavior of a corpus of programs can be used to infer the behavior of new programs. In this paper, we use a neural network to map static features associated with each branch to the probability that the branch will be taken. ESP shows significant advantages over other prediction mechanisms. Specifically, it is a program-based technique, it is effective across a range of programming languages and programming styles, and it does not rely on the use of expert-defined heuristics. 
In this paper, we describe the application of ESP to the problem of branch prediction and compare our results to existing program-based branch predictors. We also investigate the applicability of ESP across computer architectures, programming languages, compilers, and run-time systems. Averaging over a body of 43 C and Fortran programs, ESP branch prediction results in a miss rate of 20%, as compared with the 25% miss rate obtained using the best existing program-based heuristics.
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.
19177138
Reconstruction Thresholds on Regular Trees
We consider the model of broadcasting on a tree, with binary state space, on the infinite rooted tree T k in which each node has k children. The root of the tree takes a random value 0 or 1, and then each node passes a value independently to each of its children according to a 2x2 transition matrix P . We say that reconstruction is possible if the values at the d th level of the tree contain non-vanishing information about the value at the root as d→∞ . Extending a method of Brightwell and Winkler, we obtain new conditions under which reconstruction is impossible, both in the general case and in the special case p 11 =0 . The latter case is closely related to the hard-core model from statistical physics; a corollary of our results is that, for the hard-core model on the (k+1) -regular tree with activity λ=1 , the unique simple invariant Gibbs measure is extremal in the set of Gibbs measures, for any k ≥ 2 .
18962931
Analyzing xfig using the Rigi tool suite
An experiment is conducted on how well expert users of program comprehension tools are able to perform specific program understanding and maintenance tasks on the xfig drawing program using these tools. The paper reports on the experiences of the users of the Rigi reverse engineering tool suite. Rigi is an interactive, visual tool designed to help developers better understand and redocument their software. Rigi includes parsers to read the source code of the subject software and produce a graph of extracted artifacts such as procedures, variables, calls, and data accesses. To manage the complexity of the graph, an editor allows the software engineer to automatically or manually collapse related artifacts into subsystems. These subsystems typically represent concepts such as abstract data types or personnel assignments. The created hierarchy can be navigated, analyzed, and presented using various automatic or user-guided graphical layouts.