TF-IDF weighting

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Improving Native Language Identification with TF IDF Weighting

Improving Native Language Identification with TF IDF Weighting

This paper presents a Native Language Iden- tification (NLI) system based on TF-IDF weighting schemes and using linear classi- fiers - support vector machines, logistic re- gressions and perceptrons. The system was one of the participants of the 2013 NLI Shared Task in the closed-training track, achieving 0.814 overall accuracy for a set of 11 native languages. This accuracy was only 2.2 per- centage points lower than the winner’s perfor- mance. Furthermore, with subsequent evalua- tions using 10-fold cross-validation (as given by the organizers) on the combined training and development data, the best average accu- racy obtained is 0.8455 and the features that contributed to this accuracy are the TF-IDF of the combined unigrams and bigrams of words.
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Design of Thesis Topic Search Engine with Information Retrieval and Vector Space Model of TF-IDF Weighting Nilo Legowo, Sofia, Rojali Computer Science Departemetn, Binus University Jl. Kebon Jeruk Raya no.27 Jakarta 11530, Indonesia

Design of Thesis Topic Search Engine with Information Retrieval and Vector Space Model of TF-IDF Weighting Nilo Legowo, Sofia, Rojali Computer Science Departemetn, Binus University Jl. Kebon Jeruk Raya no.27 Jakarta 11530, Indonesia

(1)Search engine are made to perform search by query that consists of several words. (2)This search engine can help students who want to search reference in thesis topics of Computer Science & Mathematics. (3)Searching process is more accurate because data that searched are based on abstract, so the scope became wider. (4)Vector space model of TF-IDF weighting can provide search results that sorted by their similarity with query.

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A Study on Analysis of SMS Classification Using TF-IDF weighting

A Study on Analysis of SMS Classification Using TF-IDF weighting

In this paper, we use TF-IDF weighting model, which considers that if the term frequency is high and the term only appears in a little part of documents, then this term has a very good differen- tiate ability. This approach emphasizes the ability to differentiate different classes more, whereas it ignores the fact that the term that frequently appears in the documents belonging to the same class, can represent the characteristic of that class more[4].

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Vectorisation, Okapi et calcul de similarité pour le TAL : pour oublier enfin le TF IDF (Vectorization, Okapi and Computing Similarity for NLP : Say Goodbye to TF IDF) [in French]

Vectorisation, Okapi et calcul de similarité pour le TAL : pour oublier enfin le TF IDF (Vectorization, Okapi and Computing Similarity for NLP : Say Goodbye to TF IDF) [in French]

Vectorization, Okapi and computing similarity for NLP : say goodbye to TF-IDF In this position paper, we review a problem very common for many NLP tasks: computing similarity (or distances) between texts. We aim at showing that what is often considered as a small component in a broader complex system is very often overlooked, leading to the use of sub-optimal solutions. Indeed, computing similarity with TF-IDF weighting and cosine is often presented as “state-of-theart”, while more effective alternatives are in the Information Retrieval (IR) community. Through some experiments on several tasks, we show how this simple calculation of similarity can influence system performance. We consider two particular alternatives. The first is the weighting scheme Okapi-BM25, well known in IR and directly interchangeable with TF-IDF. The other, called vectorization, is a technique for calculating text similarities that we have developed which offers some interesting properties.
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Review on Query Focused Summarization using TF-IDF, K-Mean Clustering and HMM

Review on Query Focused Summarization using TF-IDF, K-Mean Clustering and HMM

TF-IDF weighting: Term Frequency-Inverse Document Frequency The word probability approach relies on a stop word list to eliminate too common words from consideration. Deciding which words to include in a stop list, however, is not a trivial task and assigning TF*IDF weights to words provide a better alternative. This weighting exploits counts from a background corpus, which is a large collection of documents, normally from the same genre as the document that is to be summarized; the background corpus serves as indication of how often a word may be expected to appear in an arbitrary text. The only additional information besides the term frequency c(w) that we need in order to compute the weight of a word w which appears c(w) times in the input for summarization is the number of documents, d(w), in a background corpus of D documents that contain the word. This allows us to compute the inverse document frequency the figure 1 depicts the model.
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Quick and Reliable Document Alignment via TF/IDF weighted Cosine Distance

Quick and Reliable Document Alignment via TF/IDF weighted Cosine Distance

In total, 13 research teams contributed 21 sub- missions to the shared task. The official results can be found in Table 4. Our submission ranks on 3 rd place. We would like to point out that, apart from selecting the best performing tf/idf weighting method, the training data is not used at all. Thus, besides a baseline machine translation system no additional resources are needed, which makes our approach widely applicable.

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SPAM COMMENT DETECTION IN BLOG COMMENTS FROM BLOG RSS FEED BY MODIFIED TF-IDF ALGORITHM

SPAM COMMENT DETECTION IN BLOG COMMENTS FROM BLOG RSS FEED BY MODIFIED TF-IDF ALGORITHM

Term frequency Inverse document frequency [17] works by determining the relative frequency of words in a specific Document compared to the inverse proportion of that word over the entire document corpus. Intuitively, this Calculation determines how relevant a given word is in a particular document. In our modified algorithm we calculate TF-IDF as the summation of TF-IDF of the words as a summation of bigram, trigram and quadgram similarity of the words with the document, leaving out most common words.

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Functional Characterization of the Alphavirus TF Protein

Functional Characterization of the Alphavirus TF Protein

We performed both multistep (MOI ⫽ 0.5) and one-step (MOI ⫽ 5.0) growth curves in BHK cells, C6/36 cells, and both cycling and differentiated AP-7 neuronal cells for the four mutant viruses. Representative results are shown in Fig. 2 for multistep growth curves, as the trends in one-step growth curve experiments (data not shown) were similar. Specifically, relative to the wild- type virus, cells infected with any of the four mutants demon- strated a ⱖ 1.5-log-unit reduction in infectious particle release during infection. These data suggest that the length and amino acid sequence as well as the production of TF are important for infectious particle release. In the case of the ⌬TF mutant, we ver- ified the absence of TF incorporation into virions by analyzing purified virus preparations by denaturing PAGE and silver stain- ing (Fig. 2D). To confirm the presence of both 6K and TF in the wild type and solely 6K in the ⌬TF mutant, the bands shown in Fig. 2D were removed and identified by LC-MS/MS. Indeed, the more quickly migrating band was identified as 6K, and the more slowly migrating species, absent in the lane for the ⌬ TF mutant, was identified as the TF protein (data not shown). Additionally, we estimated, using densitometry, a 50% greater concentration of TF in the purified virions relative to that of 6K (data not shown). Given previous radiodensitometry data from Gaedigk-Nitschko and Schlesinger (18), we therefore estimate ⬃16 copies of TF and ⬃ 8 copies of 6K per Sindbis virus virion. This aligns with the findings of Firth and colleagues (27). Furthermore, to interrogate the minimum length of TF required for wild-type-like infectious particle release, we constructed four additional mutants with mu- tations that modulated the length of TF such that the protein would lack either the C-terminal 17, 14, or 7 amino acids or con- tain a 4-amino-acid C-terminal extension. Interestingly, in mul- tistep growth curves in BHK cells, all of the C-terminal-truncation
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CPP, PCIT, TF-CBT: DETERMINING

CPP, PCIT, TF-CBT: DETERMINING

Trauma Processing for parent and child, parent to understand child’s experience of trauma TF-CBT: Ages 4-18 • Appropriate Supportive Adult • Identified Traumatic Experience • P[r]

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DNSSEC update TF Mobility, Vienna

DNSSEC update TF Mobility, Vienna

When the root gets signed, make sure you purge old trust anchors. 8.[r]

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A Framework For Aggregating And Retrieving Relevant Information Using TF-IDF And Term Proximity In Support Of Maize Production

A Framework For Aggregating And Retrieving Relevant Information Using TF-IDF And Term Proximity In Support Of Maize Production

In conclusion the use of TF-IDF and term proximity is an effective way of retrieving relevant information. TF-IDF with the term proximity approach discussed in this paper performs much better than the use of baseline TF-IDF. The results show evidence of the potential power of term proximity and it is quite clear that a proximity scoring function should be included in any information retrieval model to give a significant contribution to improvement of relevance in top-k document. In the future studies the framework can be improved to learn the most preferred user information based on user information usage history then factor such information in the ranking feature. It can also be improved to consider query term order of occurrence and synonyms in overall information retrieval and ranking.
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Privacy Preserving Collaborative Model Document Clustering Using TF-IDF Approach T. G. Babu *1 , E. Anitha 2

Privacy Preserving Collaborative Model Document Clustering Using TF-IDF Approach T. G. Babu *1 , E. Anitha 2

which we focus on: training a linear regression model on joint data that must be kept confidential and/or are owned by multiple parties. 5) Moreover, we want to run s[r]

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Reducing Over Weighting in Supervised Term Weighting for Sentiment Analysis

Reducing Over Weighting in Supervised Term Weighting for Sentiment Analysis

Recently the research on supervised term weighting has attracted growing attention in the field of Tradi- tional Text Categorization (TTC) and Sentiment Analysis (SA). Despite their impressive achievements, we show that existing methods more or less suffer from the problem of over-weighting. Overlooked by prior studies, over-weighting is a new concept proposed in this paper. To address this problem, two regularization techniques, singular term cutting and bias term, are integrated into our framework of su- pervised term weighting schemes. Using the concepts of over-weighting and regularization, we provide new insights into existing methods and present their regularized versions. Moreover, under the guidance of our framework, we develop a novel supervised term weighting scheme, regularized entropy (re). The proposed framework is evaluated on three datasets widely used in SA. The experimental results indicate that our re enjoys the best results in comparisons with existing methods, and regularization techniques can significantly improve the performances of existing supervised weighting methods.
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MULTICAST ROUTING_'_::tF HIERARCHICAL DATA*

MULTICAST ROUTING_'_::tF HIERARCHICAL DATA*

These methods were: (1) a single tree, with path bandwidth non-increasing from source to destination; (2) shortest paths of maximum bandwidth, which do not necessarily form a tree, in wh[r]

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Interflon Fin Lube TF

Interflon Fin Lube TF

Water hazard class 2 (German Regulation) (Self-assessment): hazardous for water Do not allow product to reach ground water, water course or sewage system. Danger to drinking water if eve[r]

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Intensity–duration–frequency (IDF) rainfall curves in Senegal

Intensity–duration–frequency (IDF) rainfall curves in Senegal

At the same time it is important to emphasize that station- arity is an elusive concept whose reality is never guaranteed in nature, even without climate change. The Sahelian rainfall regime, for instance, is known for its strong decadal variabil- ity (Le Barbé et al., 2002) with potentially great impacts on most extreme rainfall events (Panthou et al., 2013). The use of long (multi-decadal) rainfall series to fit IDF curves can thus reduce the sampling effects and reduce the IDF uncer- tainties but they can also introduce some hidden biases linked to this decadal-scale non-stationarity. This happened with the dams built on the Volta River in the 1970s. The dams were di- mensioned based on the rainfall information of the previous three decades, which included two abnormally wet decades. The reservoirs never filled up in the 1980s and 1990s. There- fore, while IDF curves are intended to be disseminated to a large community of end-users, users must be warned that they are nothing other than a decision-making support tool to be used with care and to be updated regularly.
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Risk Assessment for ITER TF Coil Manufacturing

Risk Assessment for ITER TF Coil Manufacturing

A WP has five regular double pancakes (DPs) that use a 760-m conductor and two side DPs using 430-m con- ductors. Since each conductor jacket is welded in a 13-m jacketing section, there are around 350 welds of the con- ductor jacket in a WP. On the other hand, supercritical he- lium is supplied from a helium inlet, which is located be- tween pancakes, and exits from two helium outlets at both ends of the conductor. Therefore, the helium circuit of the TF coil consists of seven conductors, seven helium inlets, and 14 helium outlets. The helium inlets and outlets are located at the bottom of the TF coil, and these areas are accessible after tokamak assembly.
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TF-CBT: Trauma-Focused Cognitive NAME:

TF-CBT: Trauma-Focused Cognitive NAME:

TF-CBT should be provided to youth who have significant emotional or behavioral difficulties related to one or more traumatic life events (including complex trauma); youth do not have to meet PTSD criteria to receive TF-CBT. TF-CBT treatment has been shown to result in improvement in PTSD symptoms, depression, anxiety symptoms, externalizing behavioral problems, sexualized behavior problems, shame, trauma- related cognitions, interpersonal trust, and social competence.

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