How Challenging is Sarcasm versus Irony Classification?: An
... versus irony classification performs 12-15% lower than sarcasm versus philosophy. Even in case of the sarcasm class, the difference is 13%. Our findings show that although these features work well for sarcasm versus philosophy classification, they do not work well for sarcasm versus irony classifica ...
... versus irony classification performs 12-15% lower than sarcasm versus philosophy. Even in case of the sarcasm class, the difference is 13%. Our findings show that although these features work well for sarcasm versus philosophy classification, they do not work well for sarcasm versus irony classifica ...
sociallocker - Projectsgoal
... For document- and sentence-level sentiment classification, there are two main types of methods in the literature: term-counting and machine learning methods. In term-counting methods, the overall orientation of a text is obtained by summing up the orientation scores of content words in the text, bas ...
... For document- and sentence-level sentiment classification, there are two main types of methods in the literature: term-counting and machine learning methods. In term-counting methods, the overall orientation of a text is obtained by summing up the orientation scores of content words in the text, bas ...
A Review of Machine Learning Algorithms for Text
... into categories and related keywords were arranged. Some facilities of digital libraries like sort by year etc were also used. The search keywords were refined to include only those words which have produced successful results. We used boolean logic for efficient searching, for example (Classificati ...
... into categories and related keywords were arranged. Some facilities of digital libraries like sort by year etc were also used. The search keywords were refined to include only those words which have produced successful results. We used boolean logic for efficient searching, for example (Classificati ...
Automated subject classification of textual web documents
... metadata, and structural information, all of which could serve as complementary features to improve automated classification. On the other hand, they are rather heterogeneous; many of them contain little tex ...
... metadata, and structural information, all of which could serve as complementary features to improve automated classification. On the other hand, they are rather heterogeneous; many of them contain little tex ...
Volumetric MRI Classification for Alzheimer`s Diseases Based on
... constructed, and their distribution parameters are used to quantify informativeness of anatomical patterns with respect to groups in recent studies12. The method has shown good performance to identify group-related structures with different occurrence likelihoods. However, it could not explain what ...
... constructed, and their distribution parameters are used to quantify informativeness of anatomical patterns with respect to groups in recent studies12. The method has shown good performance to identify group-related structures with different occurrence likelihoods. However, it could not explain what ...
Combining Clustering with Classification for Spam Detection in
... One limitation of our algorithm is that when a new user makes his first post, the same procedure of clustering, meta–feature addition, and classification, should be applied again for the whole dataset, a rather time consuming, and computationally expensive process. A suggestion would be to use incre ...
... One limitation of our algorithm is that when a new user makes his first post, the same procedure of clustering, meta–feature addition, and classification, should be applied again for the whole dataset, a rather time consuming, and computationally expensive process. A suggestion would be to use incre ...
Aiding Classification of Gene Expression Data with Feature Selection
... classes (that are to be used by the classifiers later). The first five classes are selected for this work because they represent categories of genes that are expected, on biological grounds, to exhibit similar expression profiles. They count for a total of 208 functional defined genes. Along with th ...
... classes (that are to be used by the classifiers later). The first five classes are selected for this work because they represent categories of genes that are expected, on biological grounds, to exhibit similar expression profiles. They count for a total of 208 functional defined genes. Along with th ...
Improving Semantic Role Classification with Selectional Preferences
... where the headword needs to be seen in the training data, and otherwise the SP is not satisfied. Gildea and Jurafsky (2002) showed barely significant improvements in semantic role classification of NPs for FrameNet roles using distributional clusters. In (Erk, 2007) a number of SP models are tested ...
... where the headword needs to be seen in the training data, and otherwise the SP is not satisfied. Gildea and Jurafsky (2002) showed barely significant improvements in semantic role classification of NPs for FrameNet roles using distributional clusters. In (Erk, 2007) a number of SP models are tested ...
Survey on Remotely Sensed Image Classification
... maps algorithm. All these algorithms are evaluated as per the following factors: number of clusters, size of dataset, type of dataset and type of software used. FSVM is used to enhance the SVM in reducing the effect of outliers and noises in data points and is suitable for applications, in which dat ...
... maps algorithm. All these algorithms are evaluated as per the following factors: number of clusters, size of dataset, type of dataset and type of software used. FSVM is used to enhance the SVM in reducing the effect of outliers and noises in data points and is suitable for applications, in which dat ...
Enhancing sentiment classification performance using bi
... Identifying the orientation of opinions from the text. ...
... Identifying the orientation of opinions from the text. ...
Pitch Based Sound Classification
... Nielsen, A. B., Hansen, L. K., & Kjems, U. (2006). Pitch Based Sound Classification. In 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. (Vol. 3). IEEE. DOI: 10.1109/ICASSP.2006.1660772 ...
... Nielsen, A. B., Hansen, L. K., & Kjems, U. (2006). Pitch Based Sound Classification. In 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. (Vol. 3). IEEE. DOI: 10.1109/ICASSP.2006.1660772 ...
Performance Analysis of Classifiers to Effieciently Predict Genetic
... the same class as this training instance. If multiple instances are the same (smallest) distance to the test instance,the first one found is used. The IB1 algorithm, is the simplest instance-based learning classification method [13]. IBL algorithms are derived from the nearest neighbour pattern clas ...
... the same class as this training instance. If multiple instances are the same (smallest) distance to the test instance,the first one found is used. The IB1 algorithm, is the simplest instance-based learning classification method [13]. IBL algorithms are derived from the nearest neighbour pattern clas ...
Performance Comparison of Musical Instrument
... Many feature schemes have been proposed in the literature for audio classification. It is worth to state that improving feature extraction process will be probably enhancing performance of the classification algorithm. As mention earlier, diverse features have been proposed and identified by differe ...
... Many feature schemes have been proposed in the literature for audio classification. It is worth to state that improving feature extraction process will be probably enhancing performance of the classification algorithm. As mention earlier, diverse features have been proposed and identified by differe ...
Image Texture Classification using Gray Level Co
... tones in an image [13]. Its development was inspired by the conjectured from Julesz [14] that second order probabilities were sufficient for human discrimination of texture. In general, GLCM could be computed as follows. First, an original texture image D is re-quantized into an image G with reduced ...
... tones in an image [13]. Its development was inspired by the conjectured from Julesz [14] that second order probabilities were sufficient for human discrimination of texture. In general, GLCM could be computed as follows. First, an original texture image D is re-quantized into an image G with reduced ...
New taxonomy of classification methods based on Formal Concepts
... are called, respectively, extent and intent of the concept (A, B) [7]. The set of all concepts can be organized as a complete lattice of formal concepts, called Concept Lattice [7]. ...
... are called, respectively, extent and intent of the concept (A, B) [7]. The set of all concepts can be organized as a complete lattice of formal concepts, called Concept Lattice [7]. ...
Multi-label Topic Classification of Turkish Sentences Using
... of the task is detection of the user intent and question topic from given sentences.[3]. Topic classification task has been studied a lot so far as a crucial component of dialogue systems[4,5,6] as well as it has been used for various different purposes such as enhancing information retrieval perfor ...
... of the task is detection of the user intent and question topic from given sentences.[3]. Topic classification task has been studied a lot so far as a crucial component of dialogue systems[4,5,6] as well as it has been used for various different purposes such as enhancing information retrieval perfor ...
Extending the Classification Paradigm to Temporal Domains
... [email protected] Introduction One of the primary areas of machinelearning research has been supervised concept learning - given some information about examples whose class is known, the goal is to produce a classifier which can classify examples whose class is not known.In general, research in ...
... [email protected] Introduction One of the primary areas of machinelearning research has been supervised concept learning - given some information about examples whose class is known, the goal is to produce a classifier which can classify examples whose class is not known.In general, research in ...
Dewey Decimal Classification
The Dewey Decimal Classification (DDC), or Dewey Decimal System, is a proprietary library classification system first published in the United States by Melvil Dewey in 1876. It has been revised and expanded through 23 major editions, the latest issued in 2011, and has grown from a four-page pamphlet in 1876 with fewer than 1,000 classes to a four volume set. It is also available in an abridged version suitable for smaller libraries. It is currently maintained by the Online Computer Library Center (OCLC), a library research center. OCLC licenses access to an online version, WebDewey, for catalogers, and has an experimental linked data version on the Web with open access.The Decimal Classification introduced the concepts of relative location and relative index which allow new books to be added to a library in their appropriate location based on subject. Libraries previously had given books permanent shelf locations that were related to the order of acquisition rather than topic. The classification's notation makes use of three-digit Arabic numerals for main classes, with fractional decimals allowing expansion for further detail. A library assigns a classification number that unambiguously locates a particular volume in a position relative to other books in the library, on the basis of its subject. The number makes it possible to find any book and to return it to its proper place on the library shelves. The classification system is used in 200,000 libraries in at least 135 countries.The major competing classification system to the Dewey Decimal system is the Library of Congress Classification system created by the U.S. Library of Congress.