The Experts below are selected from a list of 3396 Experts worldwide ranked by ideXlab platform
Jia Wei Han - One of the best experts on this subject based on the ideXlab platform.
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Semantic frame-based Document Representation for comparable corpora
Proceedings - IEEE International Conference on Data Mining ICDM, 2013Co-Authors: Hyungsul Kim, Xiang Ren, Yizhou Sun, Chi Wang, Jia Wei HanAbstract:Document Representation is a fundamental problem for text mining. Many efforts have been done to generate concise yet semantic Representation, such as bag-of-words, phrase, sentence and topic-level descriptions. Nevertheless, most existing techniques counter difficulties in handling monolingual comparable corpus, which is a collection of monolingual Documents conveying the same topic. In this paper, we propose the use of frame, a high-level semantic unit, and construct frame-based Representations to semantically describe Documents by bags of frames, using an information network approach. One major challenge in this Representation is that semantically similar frames may be of different forms. For example, "radiation leaked" in one news article can appear as "the level of radiation increased" in another article. To tackle the problem, a text-based information network is constructed among frames and words, and a link-based similarity measure called SynRank is proposed to calculate similarity between frames. As a result, different variations of the semantically similar frames are merged into a single descriptive frame using clustering, and a Document can then be represented as a bag of representative frames. It turns out that frame-based Document Representation not only is more interpretable, but also can facilitate other text analysis tasks such as event tracking effectively. We conduct both qualitative and quantitative experiments on three comparable news corpora, to study the effectiveness of frame-based Document Representation and the similarity measure SynRank, respectively, and demonstrate that the superior performance of frame-based Document Representation on different real-world applications.
Raymond J Spiteri - One of the best experts on this subject based on the ideXlab platform.
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Document Representation and dimension reduction for text clustering
International Conference on Data Engineering, 2007Co-Authors: Mahdi Shafiei, Singer Wang, R Zhang, Evangelos E Milios, Bin Tang, Jane E Tougas, Raymond J SpiteriAbstract:Increasingly large text damsels and the high dimensionality associated with natural language create a great challenge in text mining, In this research, a systematic study is conducted. in which three different Document Representation methods for text are used, together with three Dimension Reduction Techniques (DRT), in the context of the text clustering problem. Several standard benchmark datasets are used. The three Document Representation methods considered are based on the vector space model, and they include word, multi-word term, and character N-gram Representations. The dimension reduction methods are. independent component analysis (ICA). latent semantic indexing (LSI), and a feature selection technique based on Document Frequency (DF). Results are compared in terms of clustering performance, using the k-means clustering algorithm. Experiments show that ICA and LSI are clearly belter than DF on all darascls. For word and N-gram Representation. ICA generally gives better results compared with LSI. Experiments also show that the word Representation gives better clustering results compared to term and N-gram Representation. Finally, for the N-gram Representation, it is demonstrated that a profile length (before dimensionality reduction) of 2000 is sufficient to capture the information and in most cases, a -4-gram Representation gives better performance than 3-gram Representation.
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ICDE Workshops - Document Representation and Dimension Reduction for Text Clustering
2007 IEEE 23rd International Conference on Data Engineering Workshop, 2007Co-Authors: Shafiei, R Zhang, Evangelos E Milios, Bin Tang, Jane E Tougas, Wang, Raymond J SpiteriAbstract:Increasingly large text damsels and the high dimensionality associated with natural language create a great challenge in text mining, In this research, a systematic study is conducted. in which three different Document Representation methods for text are used, together with three Dimension Reduction Techniques (DRT), in the context of the text clustering problem. Several standard benchmark datasets are used. The three Document Representation methods considered are based on the vector space model, and they include word, multi-word term, and character N-gram Representations. The dimension reduction methods are. independent component analysis (ICA). latent semantic indexing (LSI), and a feature selection technique based on Document Frequency (DF). Results are compared in terms of clustering performance, using the k-means clustering algorithm. Experiments show that ICA and LSI are clearly belter than DF on all darascls. For word and N-gram Representation. ICA generally gives better results compared with LSI. Experiments also show that the word Representation gives better clustering results compared to term and N-gram Representation. Finally, for the N-gram Representation, it is demonstrated that a profile length (before dimensionality reduction) of 2000 is sufficient to capture the information and in most cases, a -4-gram Representation gives better performance than 3-gram Representation.
Hanshi Wang - One of the best experts on this subject based on the ideXlab platform.
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SNPD - Document Representation based on semantic smoothed topic model
2016 17th IEEE ACIS International Conference on Software Engineering Artificial Intelligence Networking and Parallel Distributed Computing (SNPD), 2016Co-Authors: Wei Song, Hanshi WangAbstract:The goal of Document Representation is to capture certain feature of the Document. Many existing Document Representation methods are based on bag-of-words and ignore semantic relevance between words in the Document. There we proposed a semantic smoothed topic model to represent Document. It takes semantic similarity into consideration for topic of Document. We conducted two experiments utilizing this method for text classification and information retrieval task. The experimental results suggest that our method is useful for capturing the semantic of text to alleviating polysemy and synonyms problem and data sparseness problem.
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Document Representation based on semantic smoothed topic model
2016 17th IEEE ACIS International Conference on Software Engineering Artificial Intelligence Networking and Parallel Distributed Computing (SNPD), 2016Co-Authors: Wei Song, Hanshi WangAbstract:The goal of Document Representation is to capture certain feature of the Document. Many existing Document Representation methods are based on bag-of-words and ignore semantic relevance between words in the Document. There we proposed a semantic smoothed topic model to represent Document. It takes semantic similarity into consideration for topic of Document. We conducted two experiments utilizing this method for text classification and information retrieval task. The experimental results suggest that our method is useful for capturing the semantic of text to alleviating polysemy and synonyms problem and data sparseness problem.
Masoud Makrehchi - One of the best experts on this subject based on the ideXlab platform.
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Handbook of Deep Learning Applications - Deep Learning for Document Representation.
Handbook of Deep Learning Applications, 2019Co-Authors: Mehran Kamkarhaghighi, Eren Gultepe, Masoud MakrehchiAbstract:While machines can discover semantic relationships in natural written language, they depend on human intervention for the provision of the necessary parameters. Precise and satisfactory Document Representation is the key to supporting computer models in accessing the underlying meaning in written language. Automated text classification, where the objective is to assign a set of categories to Documents, is a classic problem. The range of studies in text classification is varied, ranging from studying a sophisticated approach for Document Representation to developing the best possible classifiers. A common Representation approach in text classification is bag-of-words, where Documents are represented by a vector of the words that appear in each Document. Although bag-of-words is very simple to generate, the main challenge in such a presentation is that the resulting vector is very large and sparse. This sparsity and the need to ensure semantic understanding of text Documents are the major challenges in text categorization. Deep learning-based approaches provide a fixed length vector in a continuous space to represent words and Documents. This chapter reviews the available Document Representation methods that include five deep learning-based approaches: Word2Vec, Doc2Vec, GloVe, LSTM, and CNN.
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Content Tree Word Embedding for Document Representation
Expert Systems with Applications, 2017Co-Authors: Mehran Kamkarhaghighi, Masoud MakrehchiAbstract:Only humans can understand and comprehend the actual meaning that underlies natural written language, whereas machines can form semantic relationships only after humans have provided the parameters that are necessary to model the meaning. To enable computer models to access the underlying meaning in written language, accurate and sufficient Document Representation is crucial. Recently, word embedding approaches have drawn much attention in text mining research. One of the main benefits of such approaches is the use of global corpuses with the generation of pre-trained word vectors. Although very effective, these approaches have their disadvantages. Relying only on pre-trained word vectors may neglect the local context and increase word ambiguity. In this study, a new approach, Content Tree Word Embedding (CTWE), is introduced to mitigate the risk of word ambiguity and inject a local context into globally pre-trained word vectors. CTWE is basically a framework for Document Representation while using word embedding feature learning. The CTWE structure is locally learned from training data and ultimately represents the local context. While CTWE is constructed, each word vector is updated based on its location in the content tree. For the task of classification, the results show an improvement in F-score and accuracy measures when using two deep learning-based word embedding approaches, namely GloVe and Word2Vec.
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Query-relevant Document Representation for text clustering
2010 Fifth International Conference on Digital Information Management (ICDIM), 2010Co-Authors: Masoud MakrehchiAbstract:In text categorization, one well-known Document Representation is bag-of-words. Although it is simple and popular, it ignores semantics, underlying linguistic information, and word correlations. In this paper, a new Representation for text data is proposed which is called Bag-Of-Queries (BOQ). First, a taxonomy of the terms in the local vocabulary is extracted. Extracting a taxonomy is performed by learning term dependencies using an information theoretic inclusion index. Next, the taxonomy is partitioned to generate a set of correlated terms or bag of queries. Since every two partitions belong to different concepts, they are considered semantically orthogonal queries. This provides a new space of orthogonal features, which is necessary for an efficient categorization. Finally, instead of using terms as features, we use them to build a set of queries. Documents are ranked in response to the queries using a similarity measure. The similarity indices are considered as new features in a vector space model Representation. The proposed approach outperforms bag of word based clustering. It also extracts new non-redundant features and at the same time reduces dimensionality.
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ICDIM - Query-relevant Document Representation for text clustering
2010 Fifth International Conference on Digital Information Management (ICDIM), 2010Co-Authors: Masoud MakrehchiAbstract:In text categorization, one well-known Document Representation is bag-of-words. Although it is simple and popular, it ignores semantics, underlying linguistic information, and word correlations. In this paper, a new Representation for text data is proposed which is called Bag-Of-Queries (BOQ). First, a taxonomy of the terms in the local vocabulary is extracted. Extracting a taxonomy is performed by learning term dependencies using an information theoretic inclusion index. Next, the taxonomy is partitioned to generate a set of correlated terms or bag of queries. Since every two partitions belong to different concepts, they are considered seman-tically orthogonal queries. This provides a new space of orthogonal features, which is necessary for an efficient categorization. Finally, instead of using terms as features, we use them to build a set of queries. Documents are ranked in response to the queries using a similarity measure. The similarity indices are considered as new features in a vector space model Representation. The proposed approach outperforms bag of word based clustering. It also extracts new non-redundant features and at the same time reduces dimensionality.
Hyungsul Kim - One of the best experts on this subject based on the ideXlab platform.
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Semantic frame-based Document Representation for comparable corpora
Proceedings - IEEE International Conference on Data Mining ICDM, 2013Co-Authors: Hyungsul Kim, Xiang Ren, Yizhou Sun, Chi Wang, Jia Wei HanAbstract:Document Representation is a fundamental problem for text mining. Many efforts have been done to generate concise yet semantic Representation, such as bag-of-words, phrase, sentence and topic-level descriptions. Nevertheless, most existing techniques counter difficulties in handling monolingual comparable corpus, which is a collection of monolingual Documents conveying the same topic. In this paper, we propose the use of frame, a high-level semantic unit, and construct frame-based Representations to semantically describe Documents by bags of frames, using an information network approach. One major challenge in this Representation is that semantically similar frames may be of different forms. For example, "radiation leaked" in one news article can appear as "the level of radiation increased" in another article. To tackle the problem, a text-based information network is constructed among frames and words, and a link-based similarity measure called SynRank is proposed to calculate similarity between frames. As a result, different variations of the semantically similar frames are merged into a single descriptive frame using clustering, and a Document can then be represented as a bag of representative frames. It turns out that frame-based Document Representation not only is more interpretable, but also can facilitate other text analysis tasks such as event tracking effectively. We conduct both qualitative and quantitative experiments on three comparable news corpora, to study the effectiveness of frame-based Document Representation and the similarity measure SynRank, respectively, and demonstrate that the superior performance of frame-based Document Representation on different real-world applications.