10984042
TREC-9 Experiments at KAIST: QA, CLIR and Batch Filtering
392551
Finding Target Language Correspondence for Lexicalized EBMT System
This paper presents a three-phase approach to find the correspondence in Target Language (TL) sentence for a fragment of Source Language (SL) sentence in a lexicalized EBMT system. To be practical, it exploits surface information as much as possible instead of using parsers. Experiments show that, although not so perfect, it is very robust and effective. The three phases are: First, align the sentence pair at word level in order to provide anchors for phrase alignment. Second, based on the aligned anchors, find all the TL fragments which may possibly correspond to a SL fragment. And finally, using a score function, select the best TL fragment as the correspondence of a SL fragment.
9736317
Characterization of a pseudo-random testing technique for analog and mixed-signal built-in-self-test
In this paper, we characterize and evaluate the effectiveness of a pseudo-random-based implicit functional testing technique for analog and mixed-signal circuits. The analog test problem is transformed into the digital domain by embedding the device-under-test (DUT) between a digital-to-analog-converter and an analog-to-digital converter. The pseudo-random testing technique uses band-limited digital white noise (pseudo-random-patterns) as input stimulus. The signature is constructed by computing the cross-correlation between the digitized output response and the pseudo-random input sequence. We have implemented a DSP-based hardware testbed to evaluate the effectiveness of the pseudo-random testing technique. Our results show that we can achieve close to 100% yield and fault coverages by carefully selecting only two cross-correlation samples. Noise level and total harmonic distortion below 0.1% and 0.5%, respectively, do not affect the classification accuracy.
18846046
Fluid ontologies for digital museums
With the advent and accessibility of the Internet, artistic and indigenous communities are beginning to realize how digital technologies can be used as a means for documenting and preserving their histories and cultures. However, it is not yet clear what knowledge architectures are most appropriate for creating a digital museum in order to facilitate an effective collection, organization, conservation, and experience of cultural and artistic heritage. In this paper, we discuss the concept of “fluid ontologies,” a novel, dynamic structure for organizing and browsing knowledge in a digital museum. Fluid ontologies are flexible knowledge structures that evolve and adapt to communities’ interest based on contextual information articulated by human contributors, curators, and viewers, as well as artificial bots that are able to track interaction histories and infer relationships among knowledge pieces and preferences of viewers. Fluid ontologies allow for a tighter coupling between communities’ interests and the browsing structure of a digital museum. We present the key ideas behind the use of fluid ontologies within the context of digital museum design and seminal work in metadata/dynamic ontologies, particularly as it pertains to objects of cultural heritage, and discuss these characteristics in three concrete examples: (1) Village Voice, an online agora that ties together the narratives created by a group of Somali refugees using an iteration of community-designed ontologies, (2) Eventspace, a node-based collaborative archive for design activities, and (3) Tribal Peace, an online digital museum still under construction and evaluation that uses proactive agents to tie distributed Kumeyaay, Luiseno, and Cupeno reservations together in their quest to achieve greater political sovereignty .
400858
Toward good elimination orders for symbolic SAT solving
Fundamentally different from DPLL, a new approach to SAT has recently emerged that abandons search and enlists BDDs to symbolically represent clauses of the CNF. These BDDs are conjoined according to a schedule where some variables may be eliminated by quantification at each step to reduce the size of the intermediate BDDs. SAT solving then reduces to checking whether the final BDD is the zero constant. For this approach to be practical, finding a good quantification schedule is critical. We study the use of a variable elimination algorithm for this purpose, as well as two specific methods for the generation of good elimination orders based on CNF structure. While neither method appears to dominate, we show how one can heuristically select the better using the notion of width. We implement a symbolic SAT solver based on these techniques and evaluate its efficiency and robustness on a set of benchmarks against five other solvers, each having unique characteristics, including winners of the most recent SAT competition.
3032576
Generative models for name disambiguation
Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or evenshare the same name with other people. In this paper, we present an efficient framework by using two novel topic-based models, extended from Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. Experiments indicate that our approach consistently outperforms other unsupervised methods including spectral and DBSCAN clustering. Scalability is addressed by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.
474823
Boosting the Feature Space: Text Classification for Unstructured Data on the Web
The issue of seeking efficient and effective methods for classifying unstructured text in large document corpora has received much attention in recent years. Traditional document representation like bag-of-words encodes documents as feature vectors, which usually leads to sparse feature spaces with large dimensionality, thus making it hard to achieve high classification accuracies. This paper addresses the problem of classifying unstructured documents on the Web. A classification approach is proposed that utilizes traditional feature reduction techniques along with a collaborative filtering method for augmenting document feature spaces. The method produces feature spaces with an order of magnitude less features compared with a baseline bag-of-words feature selection method. Experiments on both real-world data and benchmark corpus indicate that our approach improves classification accuracy over the traditional methods for both support vector machines and AdaBoost classifiers.