You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Standard LDA model suffers the problem that the topic assignment of each word is independent and word correlation hence is neglected. To address this problem, in this paper, we propose a model called Word Related Latent Dirichlet Allocation (WR-LDA) by incorporating word correlation into LDA topic models. This leads to new capabilities that standard LDA model does not have such as estimating infrequently occurring words or multi-language topic modeling. Experimental results demonstrate the effectiveness of our model compared with standard LDA.

Cited Abstract(s):
[1]: Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

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[3]: In this paper we present a novel framework for extracting the ratable aspects of objects from online user reviews. Extracting such aspects is an important challenge in automatically mining product opinions from the web and in generating opinion-based summaries of user reviews [18, 19, 7, 12, 27, 36, 21]. Our models are based on extensions to standard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain models are more appropriate for our task since standard models tend to produce topics that correspond to global properties of objects (e.g., the brand of a product type) rather than the aspects of an object that tend to be rated by a user. The models we present not only extract ratable aspects, but also cluster them into coherent topics, e.g., 'waitress' and 'bartender' are part of the same topic 'staff' for restaurants. This differentiates it from much of the previous work which extracts aspects through term frequency analysis with minimal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve significantly upon standard topic models.

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[5]: Recent work has developed supervised methods for detecting deceptive opinion spam— fake reviews written to sound authentic and deliberately mislead readers. And whereas past work has focused on identifying individual fake reviews, this paper aims to identify offerings (e.g., hotels) that contain fake reviews. We introduce a semi-supervised manifold ranking algorithm for this task, which relies on a small set of labeled individual reviews for training. Then, in the absence of gold standard labels (at an offering level), we introduce a novel evaluation procedure that ranks artificial instances of real offerings, where each artificial offering contains a known number of injected deceptive reviews. Experiments on a novel dataset of hotel reviews show that the proposed method outperforms state-of-art learning baselines.

[6]: In this paper, we preliminarily learn the problem of reconstructing users' life history based on the their Twitter stream and proposed an unsupervised framework that create a chronological list for personal important events (PIE) of individuals. By analyzing individ- ual tweet collections, we find that what are suitable for inclusion in the personal timeline should be tweets talking about personal (as opposed to public) and time-specific (as opposed to time-general) topics. To further extract these types of topics, we introduce a non-parametric multi-level Dirichlet Process model to recognize four types of tweets: personal time-specific (PersonTS), personal time-general (PersonTG), public time-specific (PublicTS) and pub- lic time-general (PublicTG) topics, which, in turn, are used for fur- ther personal event extraction and timeline generation. To the best of our knowledge, this is the first work focused on the generation of timeline for individuals from Twitter data. For evaluation, we have built gold standard timelines that contain PIE related events from 20 ordinary twitter users and 20 celebrities. Experimental results demonstrate that it is feasible to automatically extract chronologi- cal timelines for Twitter users from their tweet collection

[7]: A significant portion of the world's text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. This paper introduces Labeled LDA, a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA's latent topics and user tags. This allows Labeled LDA to directly learn word-tag correspondences. We demonstrate Labeled LDA's improved expressiveness over traditional LDA with visualizations of a corpus of tagged web pages from del.icio.us. Labeled LDA outperforms SVMs by more than 3 to 1 when extracting tag-specific document snippets. As a multi-label text classifier, our model is competitive with a discriminative baseline on a variety of datasets.

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[9]: Social media websites provide a platform for anyone to describe significant events taking place in their lives in realtime. Currently, the majority of personal news and life events are published in a textual format, motivating information extraction systems that can provide a structured representations of major life events (weddings, graduation, etc. . . ). This paper demonstrates the feasibility of accurately extracting major life events. Our system extracts a fine-grained description of users’ life events based on their published tweets. We are optimistic that our system can help Twitter users more easily grasp information from users they take interest in following and also facilitate many downstream applications, for example realtime friend recommendation.

[10]: Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain specific synonymy as well as with polysemous words. In contrast to standard Latent Semantic Indexing (LSI) by Singular Value Decomposition, the probabilistic variant has a solid statistical foundation and defines a proper generative data model. Retrieval experiments on a number of test collections indicate substantial performance gains over direct term matching methods as well as over LSI. In particular, the combination of models with different dimensionalities has proven to be advantageous.

[11]: We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-likelihood procedure for parameter estimation, which relies on variational approximations to handle intractable posterior expectations. Prediction problems motivate this research: we use the fitted model to predict response values for new documents. We test sLDA on two real-world problems: movie ratings predicted from reviews, and web page popularity predicted from text descriptions. We illustrate the benefits of sLDA versus modern regularized regression, as well as versus an unsupervised LDA analysis followed by a separate regression.
Topic models such as Latent Dirichlet Allocation (LDA) [8] and PLSA (Probabilistic Latent Semantic Analysis) [10] have been widely used in many different fields such as social analysis [6] [9] [2] , event detection [1] [4] , text classification [11] [7] or facet mining [5] [3] . for their ability in discovering topics latent in the document collection. Standard topic models suffer the disadvantage that researchers only use word co-appearance frequency for topic modeling without considering word correlations. However similar or synonymous two terms are, if they do not co-appear in the document, they can hardly be classified into the same topic. This disadvantage is largely magnified in multi-lingual topic modeling where different languages never co-occur with each other.