Query: bag of concepts

title: Biomedical literature classification using encyclopedic knowledge: a Wikipedia based bag of concepts approach
abstract: Automatic classification of text documents into a set of categories has a lot of applications. Among those applications, the automatic classification of biomedical literature stands out as an important application for automatic document classification strategies. Biomedical staff and researchers have to deal with a lot of literature in their daily activities, so it would be useful a system that allows for accessing to documents of interest in a simple and effective way; thus, it is necessary that these documents are sorted based on some criteria that is to say, they have to be classified. Documents to classify are usually represented following the bag of words (BoW) paradigm. Features are words in the text thus suffering from synonymy and polysemy and their weights are just based on their frequency of occurrence. This paper presents an empirical study of the efficiency of a classifier that leverages encyclopedic background knowledge concretely Wikipedia in order to create bag of concepts (BoC) representations of documents, understanding concept as "unit of meaning" and thus tackling synonymy and polysemy. Besides, the weighting of concepts is based on their semantic relevance in the text. For the evaluation of the proposal, empirical experiments have been conducted with one of the commonly used corpora for evaluating classification and retrieval of biomedical information, OHSUMED, and also with a purpose built corpus of MEDLINE biomedical abstracts, UVigoMED. Results obtained show that the Wikipedia based bag of concepts representation outperforms the classical bag of words representation up to 157% in the single label classification problem and up to 100% in the multi label problem for OHSUMED corpus, and up to 122% in the single label classification problem and up to 155% in the multi label problem for UVigoMED corpus.

title: A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge* Spanish English Cross language Case Study.
abstract: The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space.  The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA) and a classifier that uses a domain specific semantic annotator (MetaMap) The corpus used for the experiments (Cross Language UVigoMED) was purpose built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts.  The performance of our approach is superior to any other state of the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p values 0.0001.  Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language specific concept spaces, a cross language classifier can be built, and it performs better than several state of the art classifiers.

title: Bag of Concepts as a Movie Genome and Representation
abstract: Author(s) Zhou, Colin Advisor(s) Dubnov,  As online retailers, media providers, and others have learned, the ability to provide quality recommendations for users is a strong profit multiplier: it drives sales, maintains retention, and provides value for users. Video streaming, retail, and video databases particularly have had extensive effort and research devoted to finding effective ways to make representations. One of these is MovieLens.org, a video recommender website that uses a representation called the tag genome to understand movies and make tuned recommendations. The tag genome representation is built from user input on the website, where users can apply tags to movies and rate them in order to provide the information needed to make recommendations. In this work, we draw from research in information retrieval to implement a bag of concepts representation for movies and make tuned recommendations in the same manner as MovieLens. Our implementation is fully unsupervised and does not require the user data needed in the implementation of MovieLens while still having similar properties to the tag genome that enable interesting tuned recommendations.

title: Automatic text classification using bag of words and bag of concepts based representations
abstract: I Declaration II Acknowledgment III Table of

title: Combining Bag of Words and Bag of Concepts representations for Arabic text classification
abstract: This paper introduces a set of new approaches for text representation for automatic classification of Arabic textual documents. These approaches are based on combining the well known Bag of Words (BOW) and the Bag of Concepts (BOC) text representation schemes and utilizing Wikipedia as a knowledge base. The proposed representations are used to generate a vector space model, which in turn is fed into a classifier to categorize a collection of Arabic textual documents. Three different machine learning based classifiers have been utilized in this work. Performance of proposed text representation models is evaluated in comparison to using a standard BOW scheme and a concept based scheme, as well as recently reported similar text representation schemes that are based on augmenting the standard BOW with the BOC.

title: Domain specific commonsense relation extraction from bag of concepts metadata
abstract: Existing semantic knowledge bases such as WordNet and Yago contain the information of relations between entities. They do not hold the information about domain specific commonsense relations between concepts like "horse" and "farm" which intuitively have close relations on semantics in the domains of image description. Metadata which is used to describe data is widespread in the data collections of various domains and can be useful resources for relation extraction. However, keywords and tags which are important form of metadata are only list of user generated words. They do not contain syntactic information which many existing works use to extract relations. In this paper we propose an approach to collect commonsense relations for specific domains by mining knowledge of global structure and internal association in the bag of concepts from metadata of data collections. We extract commonsense relations of concepts from social tags of image datasets to show the efficiency of our solution.

title: Arabic Text Classification using Bag of Concepts Representation
abstract: With the exponential growth of Arabic text in digital form, the need for efficient organization, navigation and browsing of large amounts of documents in Arabic has increased. Text Classification (TC) is one of the important subfields of data mining. The Bag of Words (BOW) representation model, which is the traditional way to represent text for TC, only takes into account the frequency of term occurrence within a document. Therefore, it ignores important semantic relationships between terms and treats synonymous words independently. In order to address this problem, this paper describes the application of a Bag of Concepts (BOC) text representation model for Arabic text. The proposed model is based on utilizing the Arabic Wikipedia as a knowledge base for concept detection. The BOC model is used to generate a Vector Space Model, which in turn is fed into a classifier to categorize a collection of Arabic text documents. Two different machine learning based classifiers have been deployed to evaluate the effectiveness of the proposed model in comparison to the traditional BOW model. The results of our experiment show that the proposed BOC model achieves an improved performance with respect to BOW in terms of classification accuracy.

title: Emotion recognition for sentences with unknown expressions based on semantic similarity by using Bag of Concepts
abstract: In studies of emotion estimation from text, varieties of methods have been attempted such as emotional expression dictionary or sentence structure dictionary and emotion corpus. However, most of these methods targeted the expressions included in the existing morphological analysis dictionaries, as a result, they did not pay enough attention to unknown words, especially newly coined words. In Japan, the growth of Internet communication sites such as weblogs and social networking sites brought younger people especially in teens and in their 20s to create new words and use them very often. We prepared an emotion corpus by collecting weblog article texts including new words, analyzed the corpus statistically, and proposed a method to estimate emotions of the texts. Most slang such as Youth Slang is too ambiguous in sense classification to be registered into the existing dictionaries such as thesaurus. To cope with these words, we created a large scale of Twitter corpus to calculate sense similarity between words. We proposed to convert unknown to sense class id to process the words that were not included in learning data. We defined this as a method using Bag of Concepts as feature. As a result of the evaluation experiment using several classifies, the proposed method was proved robustness for unknown expression.

title: A new text representation scheme combining Bag of Words and Bag of Concepts approaches for automatic text classification
abstract: This paper introduces a new approach to creating text representations and apply it to a standard text classification collections. The approach is based on supplementing the well known Bag of Words (BOW) representational scheme with a concept based representation that utilises Wikipedia as a knowledge base. The proposed representations are used to generate a Vector Space Model, which in turn is fed into a Support Vector Machine classifier to categorise a collection of textual documents from two publically available datasets. Experimental results for evaluating the performance of our model in comparison to using a standard BOW scheme and a concept based scheme, as well as recently reported similar text representations that are based on augmenting the standard BOW approach with concept based representations.

title: Bridging the Gap between Local Semantic Concepts and Bag of Visual Words for Natural Scene Image Retrieval