2158099
Keyphrase Extraction Using Semantic Networks Structure Analysis
Keyphrases play a key role in text indexing, summarization and categorization. However, most of the existing keyphrase extraction approaches require human-labeled training sets. In this paper, we propose an automatic keyphrase extraction algorithm, which can be used in both supervised and unsupervised tasks. This algorithm treats each document as a semantic network. Structural dynamics of the network are used to extract keyphrases (key nodes) unsupervised. Experiments demonstrate the proposed algorithm averagely improves 50% in effectiveness and 30% in efficiency in unsupervised tasks and performs comparatively with supervised extractors. Moreover, by applying this algorithm to supervised tasks, we develop a classifier with an overall accuracy up to 80%.
9038741
Semantic Scoring Based on Small-World Phenomenon for Feature Selection in Text Mining
This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.
3866549
Towards multi-granularity multi-facet e-book retrieval
Generally speaking, digital libraries have multiple granularities of semantic units: book, chapter, page, paragraph and word. However, there are two limitations of current eBook retrieval systems: (1) the granularity of retrievable units is either too big or too small, scales such as chapters, paragraphs are ignored; (2) the retrieval results should be grouped by facets to facilitate user's browsing and exploration. To overcome these limitations, we propose a multi-granularity multi-facet eBook retrieval approach.
904578
Simulation-based test algorithm generation for random access memories
Although there are well known test algorithms that have been used by the industry for years for testing semiconductor random-access memories (RAMs), systematic evaluation of their effectiveness and efficiency has been a difficult job. In the past, it was mainly done manually by proving a certain algorithm can detect a certain type of fault. As memory technology keeps innovating, the growing complexity of the memories and number of fault types that need to be covered will require more effective and efficient test algorithms to be discovered in much shorter time. A systematic approach for developing and evaluating memory test algorithms is thus desired. We propose such an approach here: test algorithm generation by simulation (TAGS), which generates and optimizes test algorithms, given a test time budget. Experimental results show that the algorithms generated by TAGS are more efficient than the traditional test algorithms. Using TAGS, a series of test algorithms with a detailed list of faults covered by each algorithm can be generated, providing easy trade-off between test time and fault coverage.
9984897
Structure Alignment Using Bilingual Chunking
A new statistical method called "bilingual chunking" for structure alignment is proposed. Different with the existing approaches which align hierarchical structures like sub-trees, our method conducts alignment on chunks. The alignment is finished through a simultaneous bilingual chunking algorithm. Using the constrains of chunk correspondence between source language (SL) and target language (TL), our algorithm can dramatically reduce search space, support time synchronous DP algorithm, and lead to highly consistent chunking. Furthermore, by unifying the POS tagging and chunking in the search process, our algorithm alleviates effectively the influence of POS tagging deficiency to the chunking result.The experimental results with English-Chinese structure alignment show that our model can produce 90% in precision for chunking, and 87% in precision for chunk alignment.
14142232
Confessible Threshold Ring Signatures
We present two threshold ring signature schemes with different properties. One focuses on the confessibility (or signer verifiability) and the denouncibility properties. The other focuses on the threshold-confessibility. Our schemes are built on generic ring signature schemes and can be easily adapted to most existing ring signature schemes. Based on the former works, we also construct a realization of our schemes as an example. We prove that our schemes are secure in the random oracle model.
39753705
A programmable built-in self-test core for embedded memories
195352079
The impact of network variabilities on TCP clocking schemes