1600088
Automatic Acquisition of Context-Specific Lexical Paraphrases
Lexical paraphrasing aims at acquiring word-level paraphrases. It is critical for many Natural Language Processing (NLP) applications, such as Question Answering (QA), Information Extraction (IE), and Machine Translation (MT). Since the meaning and usage of a word can vary in distinct contexts, different paraphrases should be acquired according to the contexts. However, most of the existing researches focus on constructing paraphrase corpora, in which little contextual constraints for paraphrase application are imposed. This paper presents a method that automatically acquires context-specific lexical paraphrases. In this method, the obtained paraphrases of a word depend on the specific sentence the word occurs in. Two stages are included, i.e. candidate paraphrase extraction and paraphrase validation, both of which are mainly based on web mining. Evaluations are conducted on a news title corpus and the presented method is compared with a paraphrasing method that exploits a Chinese thesaurus of synonyms -- Tongyi Cilin (Extended) (CilinE for short). Results show that the f-measure of our method (0.4852) is significantly higher than that using CilinE (0.1127). In addition, over 85% of the correct paraphrases derived by our method cannot be found in CilinE, which suggests that our method is effective in acquiring out-of-thesaurus paraphrases.
656139
HIT: Web based Scoring Method for English Lexical Substitution
This paper describes the HIT system and its participation in SemEval-2007 English Lexical Substitution Task. Two main steps are included in our method: candidate substitute extraction and candidate scoring. In the first step, candidate substitutes for each target word in a given sentence are extracted from WordNet. In the second step, the extracted candidates are scored and ranked using a web-based scoring method. The substitute ranked first is selected as the best substitute. For the multiword subtask, a simple WordNet-based approach is employed.
24687403
A synthesis scheme for simultaneous scheduling, binding, partitioning and placement with resources operating at multiple voltages
One promising technique to reduce power consumption is to power a chip with multiple supply voltages. However, as noticed by M.C. Johnson and K. Roy (see ACM Trans. Design Auto. Electronic Syst., vol.2, p.227-48, 1997), multiple voltage designs can cause a number of serious layout problems. We have shown that the layout problems can be partially solved by the addition of a partitioning step into the synthesis flow. A more subtle solution to solve the layout problems requires placement also to be included in the design flow. We present a synthesis scheme, following a simulated annealing engine, to minimize power consumption and area with resources operating at multiple voltages under timing constraints. The scheme simultaneously considers many correlated factors, such as scheduling, binding, partitioning and placement, to reduce power consumption due to both functional units and interconnections between and among them. Experiments with a number of DSP benchmarks show that the proposed algorithm can achieve significant reduction in power and area.
18794722
Word Sense Language Model for Information Retrieval
This paper proposes a word sense language model based method for information retrieval. This method, differing from most of traditional ones, combines word senses defined in a thesaurus with a classic statistical model. The word sense language model regards the word sense as a form of linguistic knowledge, which is helpful in handling mismatch caused by synonym and data sparseness due to data limit. Experimental results based on TREC-Mandarin corpus show that this method gains 12.5% improvement on MAP over traditional tf-idf retrieval method but 5.82% decrease on MAP compared to a classic language model. A combination result of this method and the language model yields 8.92% and 7.93% increases over either respectively. We present analysis and discussions on the not-so-exciting results and conclude that a higher performance of word sense language model will owe to high accurate of word sense labeling. We believe that linguistic knowledge such as word sense of a thesaurus will help IR improve ultimately in many ways.
15125228
Web Mining for Lexical Context-Specific Paraphrasing
In most applications of paraphrasing, contextual information should be considered since a word may have different paraphrases in different contexts. This paper presents a method that automatically acquires lexical context-specific paraphrases from the web. The method includes two main stages, candidate paraphrase extraction and paraphrase validation. Evaluations were conducted on a news title corpus whereby the context-specific paraphrasing method was compared with the Chinese synonymous thesaurus. Results show that the precision of our method is above 60% and the recall is above 55%, which outperforms the thesaurus significantly.
16416654
Topic Tracking Based on Keywords Dependency Profile
Topic tracking is an important task of Topic Detection and Tracking (TDT). Its purpose is to detect stories, from a stream of news, related to known topics. Each topic is "known" by its association with several sample stories that discuss it. In this paper, we propose a new method to build the keywords dependency profile (KDP) of each story and track topic basing on similarity between the profiles of topic and story. In this method, keywords of a story are selected by document summarization technology. The KDP is built by keywords co-occurrence frequency in the same sentences of the story. We demonstrate this profile can describe the core events in a story accurately. Experiments on the mandarin resource of TDT4 and TDT5 show topic tracking system basing on KDP improves the performance by 13.25% on training dataset and 7.49% on testing dataset comparing to baseline.
481284
A Novel Method for Image Spam Filtering
As spamming morphs from text-only to multi-media in content, the existing text-based spam filters are no longer effective for the task. Image analysis technology will provide an effective tool to solve the above problem. This paper proposes a novel filter approach that focuses on the spam which change the content text to image files, or embed the spam message into attached images. Spam images are analyzed and specific features are selected for inspection based on components-based method. Then the network junk-mail-filter system can use these features to identify whether to filter the mail or not by feature matching. Experiments show that our method is efficient in inspecting document images with different geometric structure. The proposed method has potential applications in document image information extraction and filtering.
2930673
Adaptive Security Dialogs for Improved Security Behavior of Users
Despite the increasing awareness of the importance of security for daily computer users, we see that many users still fail to behave securely when confronted with a security-related decision. In this paper, we introduce a new approach to security-related dialogs called Adaptive Security Dialogs (ASD). This approach is a combination of a new architecture and a new way of interacting with users to provide them with appropriate and effective security dialogs. ASD realizes this goal by matching the complexity and intrusiveness of security-related dialogs to the risk associated with the decision the user is making. This results in an architecture in which users can focus on their tasks, get (immediate) feedback on their decisions, and interact with dialogs with an appropriate complexity and appearance for the decision's associated risk. This paper makes the following three contributions. First, we introduce a general architecture for handling security-related decisions. Second, through an empirical user study using a web-based e-mail client, we show significant improvement in the care exercised by our participants without sacrificing usability. Third, we describe how the different pieces of existing research fit into the bigger picture of improving users' behavior.
8620934
Certifying Concurrent Programs Using Transactional Memory
Transactional memory (TM) is a new promising concurrency-control mechanism that can avoid many of the pitfalls of the traditional lock-based techniques. TM systems handle data races between threads automatically so that programmers do not have to reason about the interaction of threads manually. TM provides a programming model that may make the development of multi-threaded programs easier. Much work has been done to explore the various implementation strategies of TM systems and to achieve better performance, but little has been done on how to formally reason about programs using TM and how to make sure that such reasoning is sound. In this paper, we focus on the semantics of transactional memory and present a proof-carrying code (PCC) system for reasoning about programs using TM. We formalize our reasoning with respect to the TM semantics, prove its soundness, and use examples to demonstrate its effectiveness.
2385031
Semi-Supervised Multi-Task Regression
Labeled data are needed for many machine learning applications but the amount available in some applications is scarce. Semi-supervised learning and multi-task learning are two of the approaches that have been proposed to alleviate this problem. In this paper, we seek to integrate these two approaches for regression applications. We first propose a new supervised multi-task regression method called SMTR, which is based on Gaussian processes (GP) with the assumption that the kernel parameters for all tasks share a common prior. We then incorporate unlabeled data into SMTR by changing the kernel function of the GP prior to a data-dependent kernel function, resulting in a semi-supervised extension of SMTR, called SSMTR. Moreover, we incorporate pairwise information into SSMTR to further boost the learning performance for applications in which such information is available. Experiments conducted on two commonly used data sets for multi-task regression demonstrate the effectiveness of our methods.
1369350
EEG Source Localization for Two Dipoles in the Brain Using a Combined Method
Estimating the correct location of electric current source with the brain from electroencephalographic (EEG) recordings is a challenging analytic and computational problem. Specifically, there is no unique solution and solutions do not depend continuously on the data. This is an inverse problem from EEG to dipole source. In this paper we consider a method combining backpropagation neural network (BPNN) with nonlinear least square (NLS) method for source localization. For inverse problem, the BP neural network and the NLS method has its own advantage and disadvantage, so we use the BPNN to supply the initial value to the NLS method and then get the final result, here we select the Powell algorithm to do the NLS calculating. All these work are for the fast and accurate dipole source localization. The main purpose of using this combined method is to localize two dipole sources when they are locating at the same region of the brain. The following investigations are presented to show that this combined method used in this paper is an advanced approach for two dipole sources localization with high accuracy and fast calculating.