5367915
A Maximum Entropy Approach to Biomedical Named Entity Recognition
Machine learning approaches are frequently used to solve name entity (NE) recognition (NER). In this paper we propose a hybrid method that uses maximum entropy (ME) as the underlying machine learning method incorporated with dictionary-based and rule-based methods for post-processing. Simply using ME for NER, inaccurate boundary detection of NEs and misclassification may occur. Some NEs are partially recognized by ME. In the post-processing stage, we use dictionary-based and rule-based methods to extend boundary of partially recognized NEs and to adjust classification. We use GENIA corpus 3.01 to conduct 10-fold cross-verification experiments. To evaluate the performance, we consider the longest NE annotations. We evaluate our approach using standard precision (P), recall (R), and F-score, where F-score is defined as 2PR/(P+R). The precision, recall and F-score ([P, R, F]) of our ME module for overall 23 categories is [0.512, 0.538, 0.525], and after the post-processing the performance becomes [0.729, 0.711, 0.72] for [P, R, F]. For protein, DNA and RNA classes, our method achieves [P, R, F] of [0.77, 0.80, 0.785], [0.653, 0.748, 0.7], and [0.716, 0.788, 0.752], respectively. The post-processing stage significantly improves the performance of our ME-based NER module.
4516498
Using Instant Messaging to Provide an Intelligent Learning Environment
Instant Messaging enables learners and educators to interact in an on-line environment. In this paper, we propose an intelligent ChatBot system, based on instant messaging, for student on-line coaching in an English learning environment. The proposed ChatBot facilitates synchronous communication with students by using ready reference materials including, dictionaries, authorized conversation material with speaking, and a question-answering function. The agent records and analyzes conversations so that the teacher can assess students' progress. Our contribution in this paper is that we integrate the NLP Tool and AIML into an instant messaging-based ChatBot for English as a Second Language programs.
6185076
Text categorization using automatically acquired domain ontology
In this paper, we describe ontology-based text categorization in which the domain ontologies are automatically acquired through morphological rules and statistical methods. The ontology-based approach is a promising way for general information retrieval applications such as knowledge management or knowledge discovery. As a way to evaluate the quality of domain ontologies, we test our method through several experiments. Automatically acquired domain ontologies, with or without manual editing, have been used for text categorization. The results are quite satisfactory. Furthermore, we have developed an automatic method to evaluate the quality of our domain ontology.
5724385
An iterative relaxation technique for the NMR backbone assignment problem
NMR spectroscopy is one of the popular experiments to determine protein structures. An important stage of protein structure determination by using NMR is protein backbone resonance assignment (or backbone assignment for short). Due to the messiness and disorder of NMR spectral data, backbone assignment is usually a tedious and time-consuming manual work. This raises a great interest in developing an efficient and automatic method to perform backbone assignment. An iterative algorithm is proposed that is equipped with two operations: grouping and linking. Grouping is responsible for peak picking and part of connectivity determination. Ideally, those peaks with the same H/sup N/ and N chemical shifts can be grouped together; peaks belonging to the same group can be used to determine the order of two consecutive spin systems. However, in real situation, grouping is a difficult task due to false positives and false negatives. We sometimes add hypothetic peaks to tackle false negatives and use linking operation to remove false positives. Linking is responsible for part of connectivity determination and backbone assignment. Given a protein sequence and partial connectivity information, we try to link connected spin systems as much as possible. False positive spin systems may create conflicts in the linking stage. To find a good assignment on a noisy dataset, the backbone assignment problem is modeled as a maximum independent set problem. Although the problem is NP-complete, there are heuristic methods that obtain pretty good results.
1251407
Reference metadata extraction using a hierarchical knowledge representation framework
The integration of bibliographical information on scholarly publications available on the Internet is an important task in the academic community. Accurate reference metadata extraction from such publications is essential for the integration of metadata from heterogeneous reference sources. In this paper, we propose a hierarchical template-based reference metadata extraction method for scholarly publications. We adopt a hierarchical knowledge representation framework called INFOMAP, which automatically extracts metadata. The experimental results show that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference styles with a high degree of precision. The overall average accuracy is 92.39% for the six major reference styles compared in this study.
269822
Visual Cue Cluster Construction via Information Bottleneck Principle and Kernel Density Estimation
Recent research in video analysis has shown a promising direction, in which mid-level features (e.g., people, anchor, indoor) are abstracted from low-level features (e.g., color, texture, motion, etc.) and used for discriminative classification of semantic labels. However, in most systems, such mid-level features are selected manually. In this paper, we propose an information-theoretic framework, visual cue cluster construction (VC3), to automatically discover adequate mid-level features. The problem is posed as mutual information maximization, through which optimal cue clusters are discovered to preserve the highest information about the semantic labels. We extend the Information Bottleneck framework to high-dimensional continuous features and further propose a projection method to map each video into probabilistic memberships over all the cue clusters. The biggest advantage of the proposed approach is to remove the dependence on the manual process in choosing the mid-level features and the huge labor cost involved in annotating the training corpus for training the detector of each mid-level feature. The proposed VC3 framework is general and effective, leading to exciting potential in solving other problems of semantic video analysis. When tested in news video story segmentation, the proposed approach achieves promising performance gain over representations derived from conventional clustering techniques and even the mid-level features selected manually.
38278026
A Simple Test for the Consecutive Ones Property
A (0,1)-matrix satisfies the consecutive ones property if there exists a column permutation such that the ones in each row of the resulting matrix are consecutive. Booth and Lueker (1976, J. Comput. System Sci.13, 335?378) designed a linear time-testing algorithm for this property based on a data structure called “PQ-trees.” This procedure is quite complicated and the linear-time-amortized analysis is also rather involved. We developed an off-line linear time test for the consecutive ones property without using PQ-trees and the corresponding template matching, which makes ours considerably simpler. A simplification of the consecutive ones test will immediately simplify algorithms (and computer codes) for interval-graph and planar-graph recognition. Our approach is based on a decomposition technique that separates the rows into prime subsets, each of which admits essentially a unique column ordering that realizes the consecutive ones property. The success of this approach is based on finding a good “row ordering” to be tested iteratively.
7586483
Video search reranking via information bottleneck principle
We propose a novel and generic video/image reranking algorithm, IB reranking, which reorders results from text-only searches by discovering the salient visual patterns of relevant and irrelevant shots from the approximate relevance provided by text results. The IB reranking method, based on a rigorous Information Bottleneck (IB) principle, finds the optimal clustering of images that preserves the maximal mutual information between the search relevance and the high-dimensional low-level visual features of the images in the text search results. Evaluating the approach on the TRECVID 2003-2005 data sets shows significant improvement upon the text search baseline, with relative increases in average performance of up to 23%. The method requires no image search examples from the user, but is competitive with other state-of-the-art example-based approaches. The method is also highly generic and performs comparably with sophisticated models which are highly tuned for specific classes of queries, such as named-persons. Our experimental analysis has also confirmed the proposed reranking method works well when there exist sufficient recurrent visual patterns in the search results, as often the case in multi-source news videos.
17900842
Fast cache access with full-map block directory
There are two concurrent paths in a typical cache access -one through the data array and the other through the tag array. In most cases, the path through the tag array is significantly longer than that through the data array. In this paper, we propose a new scheme that exploits this imbalance in the tag and data paths to improve overall cache performance. Under this scheme, an additional tag directory, the full-map block directory, is used to provide an alternate tag path to speed up cache access for almost all the memory requests. This scheme is based on the observation that spatial locality exists on a cache line basis i.e. cache lines near one another tend to be referenced together. Performance evaluation using the TPC-C benchmark and the SPEC92 benchmark suite demonstrates that this scheme has the potential to improve overall system performance by more than 20%.
10542450
Capturing dynamic memory reference behavior with adaptive cache topology
Memory references exhibit locality and are therefore not uniformly distributed across the sets of a cache. This skew reduces the effectiveness of a cache because it results in the caching of a considerable number of less-recently-used lines which are less likely to be re-referenced before they are replaced. In this paper, we describe a technique that dynamically identifies these less-recently-used lines and effectively utilizes the cache frames they occupy to more accurately approximate the global least-recently-used replacement policy while maintaining the fast access time of a direct-mapped cache. We also explore the idea of using these underutilized cache frames to reduce cache misses through data prefetching. In the proposed design, the possible locations that a line can reside in is not predetermined. Instead, the cache is dynamically partitioned into groups of cache lines. Because both the total number of groups and the individual group associativity adapt to the dynamic reference pattern, we call this design the adaptive group-associative cache. Performance evaluation using trace-driven simulations of the TPC-C benchmark and selected programs from the SPEC95 benchmark suite shows that the group-associative cache is able to achieve a hit ratio that is consistently better than that of a 4-way set-associative cache. For some of the workloads, the hit ratio approaches that of a fully-associative cache.