1,985 research outputs found
The Development Impact of Information Technology in Trade Facilitation
No full textThe main purpose of this chapter is to provide an overview and context of the country studies on Information Technology (IT) for Trade Facilitation (TF) in Small and Medium Enterprises (SMEs).Impact of Information Techonology, Trade Facilitation, SMEs
Evaluation of ITER TF Coil Joint Performance
No full textTo evaluate the ITER TF joint performance, the joint test sample, which consists of two short TF conductors and has full size joint, shall be tested using NIFS test facility under the condition of current of 68 kA and external field of 2 T. For high accuracy, the issue of voltage difference between cable and jacket had been anticipated in the evaluation of joint resistance. If a voltage difference exist between them, it is difficult to measure real joint resistance using voltage taps on the jacket. Therefore, the author first calculated the position where voltage of cable and jacket become equipotential and then decided the voltage tap position where the influence of voltage drop could be avoided. Thus, a high accuracy measurement of joint resistance could be achieved and the joint resistance was accurately evaluated as around 1 n Ω , which is well below the ITER requirement of 3 n Ω .journal articl
Lightweight Spoken Utterance Classification with CFG, tf-idf and Dynamic Programming
No full textWe describe a simple spoken utterance classification method suitable for data-sparse domains which can be approximately described by CFG grammars. The central idea is to perform robust matching of CFG rules against output from a large-vocabulary recogniser, using a dynamic programming method which optimises the tf-idf score of the matched grammar string. We present results of experiments carried out on a substantial CFG-based medical speech translator and the publicly available Spoken CALL Shared Task. Robust utterance classification using the tf-idf method strongly outperforms plain CFG-based recognition for both domains. When comparing with Naive Bayes classifiers trained on data sampled from the CFG grammars, the tf-idf/dynamic programming method is much better on the complex speech translation domain, but worse on the simple Spoken CALL Shared Task domain
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks
No full textTerm frequency-inverse document frequency, or TF-IDF for short, and its many variants form a class of term weighting functions the members of which are widely used in text analysis applications. While TF-IDF was originally proposed as a heuristic, theoretical justifications grounded in information theory, probability, and the divergence from randomness paradigm have been advanced. In this work, we present an empirical study showing that TF-IDF corresponds very nearly with the hypergeometric test of statistical significance on selected real-data document retrieval, summarization, and classification tasks. These findings suggest that a fundamental mathematical connection between TF-IDF and the negative logarithm of the hypergeometric test P-value (i.e., a hypergeometric distribution tail probability) remains to be elucidated. We advance the empirical analyses herein as a first step toward explaining the long-standing effectiveness of TF-IDF from a statistical significance testing lens. It is our aspiration that these results will open the door to the systematic evaluation of significance testing derived term weighting functions in text analysis applications
Who said that? Comparing performance of TF-IDF and fastText to identify authorship of short sentences
No full textAuthorship identification is often applied to large documents, but less so to short, everyday sentences. The ability of identifying who said a short line could provide help to chatbots or personal assistants. This research compares performance of TF-IDF and fastText when identifying authorship of short sentences, by applying these feature extraction techniques to the television series Friends' transcripts. TF-IDF outperforms fastText in every measurement, but its performance is only marginally better than randomly guessing the original character, reaching an accuracy of 28 percent when making a distinction between 6 characters. Accuracy increases linearly at the same rate for both techniques as the minimum word count per sentence set on the test data increases. TF-IDF's confidence remains constant as this limit is set on either the test or training data, whereas fastText's confidence decreases and increases, respectively. Cross-entropy loss, however, remains constant for fastText and decreases for TF-IDF as the minimum word count set on the test data increases.CSE3000 Research ProjectComputer Science and Engineerin
Evaluation of ITER TF Coil Joint Performance
No full textTo evaluate the ITER TF joint performance, the joint test sample, which consists of two short TF conductors and has full size joint, shall be tested using NIFS test facility under the condition of current of 68 kA and external field of 2 T. For high accuracy, the issue of voltage difference between cable and jacket had been anticipated in the evaluation of joint resistance. If a voltage difference exist between them, it is difficult to measure real joint resistance using voltage taps on the jacket. Therefore, the author first calculated the position where voltage of cable and jacket become equipotential and then decided the voltage tap position where the influence of voltage drop could be avoided. Thus, a high accuracy measurement of joint resistance could be achieved and the joint resistance was accurately evaluated as around 1 n Ω , which is well below the ITER requirement of 3 n Ω
Extracting location context from transcripts: a comparison of ELMo and TF-IDF
No full textUsing transcripts of the TV-series FRIENDS, this paper explores the problem of predicting the location in which a sentence was said. The research focuses on using feature extraction on the sentences, and training a logistic regression model on those features. Specifically looking at the differences in performance between using ELMo and TF-IDF for this feature extraction, achieving an accuracy rate of 58\% and 67\% respectively on a binary classification. The paper also explores the effect of several data cleaning techniques on the results. Git repository containing the source code used in the paper - https://github.com/David-Happel/scene-location-NLPCSE3000 Research ProjectComputer Science and Engineerin
Author identification system
No full textAbstract: Every one of us has different approach to speak and write, and there exists a long history of linguistic and stylistic analysis into authorship attribution. In last year’s, practical application for author identification have grown in area such as computer forensic(linking intercepted message to each other and to find rebel), criminal law(identifying author of payoff notes and harassing letter), civil law and computer security (tracking author of computer source code). This paper proposes the implementation of author identification system. This proposed system is based upon the principles and concepts of text analysis. For ensuring maximum accuracy in identifying author of the document we will be using TF-IDF algorithm which consists of extraction of features from the text, scoring these features and comparing them with a set of scores stored in the corpus
CTIP2 colocalizes with Sp1 and COUP-TF within Hp1α-associated structures
No full text<p><b>Copyright information:</b></p><p>Taken from "COUP-TF interacting protein 2 represses the initial phase of HIV-1 gene transcription in human microglial cells"</p><p>Nucleic Acids Research 2005;33(7):2318-2331.</p><p>Published online 22 Apr 2005</p><p>PMCID:PMC1084325.</p><p>© The Author 2005. Published by Oxford University Press. All rights reserved</p> () Microglial cells were transfected or not with Flag-CTIP2 as indicated. After being treated, endogenous Sp1, COUP-TF and Hp1α proteins were immunodetected with primary anti-COUP-TF (Santa Cruz Biotechnology) (images 1 and 2), anti-Sp1 (images 6 and 7) and anti-Hp1α antibodies (images 11 and 12). Overexpressed Flag-CTIP2 was detected with antibodies directed against the Flag epitope (images 3, 8 and 13). The primary immunocomplexes were revealed by CY2- or CY3-labeled anti-species secondary antibodies (green or red staining). Mask column (images 5, 10 and 15) shows the colocalized CY2 and CY3 stainings. () Microglial cells expressing RFP-CTIP2 (image 1) and GFP-Sp1 (image 3) were subjected to endogenous COUP-TF immunodetection with anti-COUP-TF antibodies (kindly provided by J. E. Mertz). COUP-TF immunocomplexes were stained by CY5- (blue staining) labeled anti-species secondary antibodies (image 2). Pattern of RFP-CTIP2 and GFP-Sp1 expressed alone are presented on images 5 and 6, respectively. ( and ) Coverslips were subjected to confocal microscopy analysis. Bar, 10 μm
Using TF-IDF n-gram and word embedding cluster ensembles for author profiling: Notebook for PAN at CLEF 2017
No full textThis paper presents our approach and results for the 2017 PAN Author Profiling Shared Task. Language-specific corpora were provided for four langauges: Spanish, English, Portuguese, and Arabic. Each corpus consisted of tweets authored by a number of Twitter users labeled with their gender and the specific variant of their language which was used in the documents (e.g. Brazilian or European Portuguese). The task was to develop a system to infer the same attributes for unseen Twitter users. Our system employs an ensemble of two probabilistic classifiers: a Logistic regression classifier trained on TF-IDF transformed n-grams and a Gaussian Process classifier trained on word embedding clusters derived for an additional, external corpus of tweets
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