3,711 research outputs found
Unsupervised multi-author document decomposition based on hidden Markov model
© 2016 Association tor Computational Linguistics. This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components. The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships. For this purpose, we adopt Hidden Markov Model (HMM) and construct a sequential probabilistic model to capture the dependencies of sequential sentences and their authorships. An unsupervised learning method is developed to initialize the HMM parameters. Experimental results on benchmark datasets have demonstrated the significant benefit of our idea and our approach has outperformed the state-of-the-arts on all tests. As an example of its applications, the proposed approach is applied for attributing authorship of a document and has also shown promising results
SUDMAD: Sequential and unsupervised decomposition of a multi-author document based on a hidden markov model
© 2017 ASIS & T. Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues), and intelligence issues that involve disputed anonymous documents. Among existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big room for improvement. In this paper, we consider the contextual correlation hidden among sentences and propose an algorithm for Sequential and Unsupervised Decomposition of a Multi-Author Document (SUDMAD) written in any language, disregarding topics, through the construction of a Hidden Markov Model (HMM) reflecting the authors’ writing styles. To build and learn such a model, an unsupervised, statistical approach is first proposed to estimate the initial values of HMM parameters of a preliminary model, which does not require the availability of any information of author’s or document’s context other than how many authors contributed to writing the document. To further boost the performance of this approach, a boosted HMM learning procedure is proposed next, where the initial classification results are used to create labeled training data to learn a more accurate HMM. Moreover, the contextual relationship among sentences is further utilized to refine the classification results. Our proposed approach is empirically evaluated on three benchmark datasets that are widely used for authorship analysis of documents. Comparisons with recent state-of-the-art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach
Multi-author document decomposition based on authorship
University of Technology Sydney. Faculty of Engineering and Information Technology.Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues) and intelligence issues that involves disputed anonymous documents. Among the existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big rooms for improvement. In this thesis, we propose novel approaches for decomposition of a multi-author document written in any language disregarding to topics, based on a Naive-Bayesian model and Hidden Markov Model (HMM). The proposed approaches of the Naive-Bayesian model aim to exploit the difference in its posterior probability to improve the performance of decomposition. Two main procedures are proposed based on Naive-Bayesian model, and they are Segment Elicitation procedure and Probability Indication Procedure. The segment elicitation procedure is proposed to form a strong labeled training dataset. The probability indication procedure is developed to improve the purity of the sentence decomposition. The proposed approaches of the HMM strive to exploit the contextual correlation hidden among sentences when determining their authorships. In this thesis, it is for the first time the sequential patterns hidden among document elements is considered for such a problem. To build and learn the HMM, a new unsupervised learning method is proposed to estimate its initial parameters. The proposed frameworks do not require the availability of any information of authors or document's context other than how many authors have contributed to writing the document. The effectiveness of the proposed algorithms is proved using benchmark datasets which are widely used for authorship analysis of documents. Furthermore, scientific papers are used to demonstrate the performance of the proposed approaches on authentic documents. Comparisons with recent state-the-art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of the proposed approaches
A Unified Multilingual Handwriting Recognition System using multigrams sub-lexical units
We address the design of a unified multilingual system for handwriting
recognition. Most of multi- lingual systems rests on specialized models that
are trained on a single language and one of them is selected at test time.
While some recognition systems are based on a unified optical model, dealing
with a unified language model remains a major issue, as traditional language
models are generally trained on corpora composed of large word lexicons per
language. Here, we bring a solution by con- sidering language models based on
sub-lexical units, called multigrams. Dealing with multigrams strongly reduces
the lexicon size and thus decreases the language model complexity. This makes
pos- sible the design of an end-to-end unified multilingual recognition system
where both a single optical model and a single language model are trained on
all the languages. We discuss the impact of the language unification on each
model and show that our system reaches state-of-the-art methods perfor- mance
with a strong reduction of the complexity.Comment: preprin
Sequential and unsupervised document authorial clustering based on hidden markov model
© 2017 IEEE. Document clustering groups documents of certain similar characteristics in one cluster. Document clustering has shown advantages on organization, retrieval, navigation and summarization of a huge amount of text documents on Internet. This paper presents a novel, unsupervised approach for clustering single-author documents into groups based on authorship. The key novelty is that we propose to extract contextual correlations to depict the writing style hidden among sentences of each document for clustering the documents. For this purpose, we build an Hidden Markov Model (HMM) for representing the relations of sequential sentences, and a two-level, unsupervised framework is constructed. Our proposed approach is evaluated on four benchmark datasets, widely used for document authorship analysis. A scientific paper is also used to demonstrate the performance of the approach on clustering short segments of a text into authorial components. Experimental results show that the proposed approach outperforms the state-of-the-art approaches
A Spectral Algorithm for Latent Dirichlet Allocation
The problem of topic modeling can be seen as a generalization of the
clustering problem, in that it posits that observations are generated due to
multiple latent factors (e.g., the words in each document are generated as a
mixture of several active topics, as opposed to just one). This increased
representational power comes at the cost of a more challenging unsupervised
learning problem of estimating the topic probability vectors (the distributions
over words for each topic), when only the words are observed and the
corresponding topics are hidden.
We provide a simple and efficient learning procedure that is guaranteed to
recover the parameters for a wide class of mixture models, including the
popular latent Dirichlet allocation (LDA) model. For LDA, the procedure
correctly recovers both the topic probability vectors and the prior over the
topics, using only trigram statistics (i.e., third order moments, which may be
estimated with documents containing just three words). The method, termed
Excess Correlation Analysis (ECA), is based on a spectral decomposition of low
order moments (third and fourth order) via two singular value decompositions
(SVDs). Moreover, the algorithm is scalable since the SVD operations are
carried out on matrices, where is the number of latent factors
(e.g. the number of topics), rather than in the -dimensional observed space
(typically ).Comment: Changed title to match conference version, which appears in Advances
in Neural Information Processing Systems 25, 201
Efficient Decomposed Learning for Structured Prediction
Structured prediction is the cornerstone of several machine learning
applications. Unfortunately, in structured prediction settings with expressive
inter-variable interactions, exact inference-based learning algorithms, e.g.
Structural SVM, are often intractable. We present a new way, Decomposed
Learning (DecL), which performs efficient learning by restricting the inference
step to a limited part of the structured spaces. We provide characterizations
based on the structure, target parameters, and gold labels, under which DecL is
equivalent to exact learning. We then show that in real world settings, where
our theoretical assumptions may not completely hold, DecL-based algorithms are
significantly more efficient and as accurate as exact learning.Comment: ICML201
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