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Depression raises a double challenge: besides the negative mood and the intrusive thoughts, the relation to the self also becomes difficult. Online forums are analysed as communicative platforms enabling the interactive reconstruction of the self. The discourses of online depression forums are explored. Firstly, narrative patterns are identified according to their thematic focus (e.g. dysfunctional body, challenges of intimacy) and discursive logic (e.g. information exchange, support). Secondly, narratives are analysed in order to describe various ways of grounding a depressed self. ∼70.000 depression-related posts from the biggest English-speaking online forums (e.g. , ) were analysed. Quantitative (LDA topic modelling) and qualitative (deep reading) approaches were used simultaneously to determine the optimal number of topics and their interpretation. 13 topics were identified and interpreted according to their content and communicative function. Based on the inter-topic distances four clusters were identified (medicalized, intimacy-oriented, critical and uninhabitable self-narratives). The clusters of the 13 topics highlight various ways of narrating depression and the depressed self. Based on a comparison with a systematic review of mental illness recovery narratives, depression forums cover most narrative genres and emotional tones, thus create a unique opportunity for integrating the depressing experiences in the self. [ABSTRACT FROM AUTHOR]

While recent developments of neural network (NN) models have led to a series of record-breaking achievements in many applications, the lack of sufficiently good datasets remains a problem for some applications. For such a problem, we can however exploit a large number of unstructured text corpora as an external knowledge to complement the training data, and most prevailing neural network solutions employ word embedding methods for such purposes. In this paper, we propose LDA-Reg, a novel knowledge driven regularization framework based on Latent Dirichlet Allocation (LDA) as an alternative to the word embedding methods to adaptively utilize abundant external knowledge and to interpret the NN model. For the joint learning of the parameters, we propose EM-SGD, an effective update method which incorporates Expectation Maximization (EM) and Stochastic Gradient Descent (SGD) to update parameters iteratively. Moreover, we also devise a lazy update and sparse update method for the high-dimensional inputs and sparse inputs respectively. We validate the effectiveness of our regularization framework through an extensive experimental study over real world and standard benchmark datasets. The results show that our proposed framework not only achieves significant improvement over state-of-the-art word embedding methods but also learns interpretable and significant topics for various tasks. [ABSTRACT FROM AUTHOR]

Quantitative text analysis is a set of procedures for grouping documents and the words and expressions that make up the content of documents, supported by statistical methods and tools. Its main advantage is that in addition to the information that can be extracted from numerical data, it provides a solution for accessing additional information that previously seems unavailable. Thus, text analysis is most commonly used where the text contains more information than could be obtained from quantified data. Text and content analysis is today one of the most exciting and focused areas of data mining and data analysis, both in terms of mathematical, statistical, methodological, and software fit. In this study, latent Dirichlet allocation (LDA) is presented as a possible methodological approach through a concrete example. The primary aim of the article is to show that text analysis can provide long-term and novel perspectives in the fields of management science. [ABSTRACT FROM AUTHOR]

As one of the fundamental information extraction methods, topic model has been widely used in text clustering, information recommendation and other text analysis tasks. Conventional topic models mainly utilize word co-occurrence information in texts for topic inference. However, it is usually hard to extract a group of words that are semantically coherent and have competent representation ability when the models applied into short texts. It is because the feature space of the short texts is too sparse to provide enough co-occurrence information for topic inference. The continuous development of word embeddings brings new representation of words and more effective measurement of word semantic similarity from concept perspective. In this study, we first mine word co-occurrence patterns (i.e., biterms) from short text corpus and then calculate biterm frequency and semantic similarity between its two words. The result shows that a biterm with higher frequency or semantic similarity usually has more similar words in the corpus. Based on the result, we develop a novel probabilistic topic model, named Noise Biterm Topic Model with Word Embeddings (NBTMWE). NBTMWE extends the Biterm Topic Model (BTM) by introducing a noise topic with prior knowledge of frequency and semantic similarity of biterm. NBTMWE shows the following advantages compared with BTM: (1) It can distinguish meaningful latent topics from a noise topic which consists of some common-used words that appear in many texts of the dataset; (2) It can promote a biterm's semantically related words to the same topic during the sampling process via generalized P ó l y a Urn (GPU) model. Using auxiliary word embeddings trained from a large scale of corpus, we report the results testing on two short text datasets (i.e., Sina Weibo and Web Snippets). Quantitatively, NBTMWE outperforms the state-of-the-art models in terms of coherence, topic word similarity and classification accuracy. Qualitatively, each of the topics generated by NBTMWE contains more semantically similar words and shows superior intelligibility. [ABSTRACT FROM AUTHOR]

• Spatiotemporal usage patterns of station-free shared bikes are explored. • A topic-based two-stage algorithm is proposed to discover city functional regions. • Region functions are labeled by static POI data and dynamic mobility patterns. Station-free shared bike (SFSB) is a new travel mode that shared bikes are allowed to park in any proper places. It implies that the users are more likely to park the SFSB as close as their destinations. This advantage makes the SFSB data to be an ideal source to understand the land-use distribution. Inspired by the idea in text mining, this paper proposes a topic-based two-stage SFSB data mining algorithm to understand the SFSB user's travel behavior and to discover city functional regions. A region, a function and human mobility patterns are treated as a document, a topic and words, respectively. Then, a region is represented by a distribution of functions, and a function is featured by a distribution of mobility patterns. The point-of-interest data is combined to annotate the clustered regions to discover the real functions. At last, the proposed method is tested using 14-day SFSB data in Beijing and the results are estimated by the Satellite Map data. The proposed methods and the results can be applied to infer the individual's travel purpose through functional regions and to improve land-use planning, etc. [ABSTRACT FROM AUTHOR]

In networks, multiple contagions, such as information and purchasing behaviors, may interact with each other as they spread simultaneously. However, most of the existing information diffusion models are built on the assumption that each individual contagion spreads independently, regardless of their interactions. Gaining insights into such interaction is crucial to understand the contagion adoption behaviors, and thus can make better predictions. In this paper, we study the contagion adoption behavior under a set of interactions, specifically, the interactions among users, contagions' contents, and sentiments, which are learned from social network structures and texts. We develop an effective and efficient interaction-aware diffusion (IAD) framework, incorporating these interactions into a unified model. We also present a generative process to distinguish user roles, a co-training method to determine contagions' categories and a new topic model to obtain topic-specific sentiments. Evaluation on the large-scale Weibo dataset demonstrates that our proposal can learn how different users, contagion categories, and sentiments interact with each other efficiently. With these interactions, we can make a more accurate prediction than the state-of-art baselines. Moreover, we can better understand how the interactions influence the propagation process and thus can suggest useful directions for information promotion or suppression in viral marketing. [ABSTRACT FROM AUTHOR]

The volume of short text data increases rapidly these years. Data examples include tweets and online Q&A pairs. It is essential to organize and summarize these data automatically. Topic model is one of the effective approaches, whose application domains include text mining, personalized recommendation and so on. Conventional models like pLSA and LDA are designed for long text data. However, these models may suffer from the sparsity problem brought by lacking words in short text scenarios. Recent studies such as BTM show that using word co-occurrent pairs is effective to relieve the sparsity problem. However, both BTM and extended models ignore the quantifiable relationship between words. From our perspectives, two more related words should occur in the same topic. Based on this idea, we introduce a model named RIBS, which makes use of RNN to learn relationship. By using the learned relationship, we introduce a model named RIBS-Bigrams, which can display topics with bigrams. Through experiments on two open-source and real-world datasets, RIBS achieves better coherence in topic discovery, and RIBS-Bigrams achieves better readability in topic display. In the document characterization task, the document representation of RIBS can lead better purity and entropy in clustering, higher accuracy in classification. [ABSTRACT FROM AUTHOR]

A phrase is a natural, meaningful, and essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. However, from phrase quality and topical cohesion perspectives, the outcomes of existing approaches remain to be improved. Usually, the process of topical phrase mining is twofold: phrase mining and topic modeling. For phrase mining, existing approaches often suffer from order sensitive and inappropriate segmentation problems, which make them often extract inferior quality phrases. For topic modeling, traditional topic models do not fully consider the constraints induced by phrases, which may weaken the cohesion. Moreover, existing approaches often suffer from losing domain terminologies since they neglect the impact of domain-level topical distribution. In this paper, we propose an efficient method for high quality and cohesive topical phrase mining. A high quality phrase should satisfy frequency, phraseness, completeness, and appropriateness criteria. In our framework, we integrate quality guaranteed phrase mining method, a novel topic model incorporating the constraint of phrases, and a novel document clustering method into an iterative framework to improve both phrase quality and topical cohesion. We also describe efficient algorithmic designs to execute these methods efficiently. The empirical verification demonstrates that our method outperforms the state-of-the-art methods from the aspects of both interpretability and efficiency. [ABSTRACT FROM AUTHOR]

Topic and sentiment joint modelling has been successfully used in sentiment analysis for product reviews. However, the problem of text sparse is universal with the widespread smart devices and the shorter product reviews. In this paper, we propose a joint sentiment-topic model WSTM (Word-pair Sentiment-Topic Model) for the short text reviews, detecting sentiments and topics simultaneously from the text, especially considering the text sparse problem. Unlike other topic models modelling the generative process of each document, our directly models the generation of the word-pair set from the whole global corpus. In the generative process of WSTM, all of the words in a sentence have the same sentiment polarity, and two words in a word-pair have the same topic. We apply WSTM to two real-life Chinese product review datasets to verify its performance. In three experiments, compared with the existing approaches, the results demonstrate WSTM is quantitatively effective on both topic discovery and document level sentiment. [ABSTRACT FROM AUTHOR]

In traditional Chinese medicine (TCM), prescriptions are the daughters of doctors’ clinical experiences, which have been the main way to cure diseases in China for several thousand years. In the long Chinese history, a large number of prescriptions have been invented based on TCM theories. Regularities in the prescriptions are important for both clinical practice and novel prescription development. Previous works used many methods to discover regularities in prescriptions, but rarely described how a prescription is generated using TCM theories. In this work, we propose a topic model which characterizes the generative process of prescriptions in TCM theories and further incorporate domain knowledge into the topic model. Using 33,765 prescriptions in TCM prescription books, the model can reflect the prescribing patterns in TCM. Our method can outperform several previous topic models and group recommendation methods on generalization performance, herbs recommendation, symptoms suggestion, and prescribing patterns discovery. [ABSTRACT FROM PUBLISHER]

It is useful to extract review sentences based on an assigned viewpoint for purposes such as summarization tasks. Previous studies have considered review extraction using semisupervised learning or association mining. However, we approach this task using a clustering method. In particular, we focus on a topic model as a clustering method. In the conventional topic model, after randomly initializing the word distribution and the topic distribution, these distributions are estimated in order to minimize the perplexity using Gibbs sampling or variational Bayes. We introduce a new method called the PageRank topic model (PRTM) for estimating multinomial distributions over topics and words using network structure analysis methods. PRTM extracts topics by focusing on the co-occurrence relationships of words and it does not need randomly initialized values. Therefore, it can calculate unique word and topic distributions. In experiments using synthetic data, we showed that PRTM can infer an appropriate number of topics by clustering short sentences, and it was particularly effective when the sentences were covered by a small number of topics. Furthermore, in a real-world review data experiment, we showed that PRTM performed better with a shorter runtime compared with other models that infer the number of topics. [ABSTRACT FROM AUTHOR]

The content in social media is difficult to analyze because of its informal and unstructured features. Luckily, some social media data like tweets have rich hashtags information, which can be helpful to identify meaningful content and topic information. More importantly, the hashtag usually express the context information of a tweet best. To this end, this paper introduces a context-aware topic model to detect and track the evolution of content by integrating hashtag and time information in text-based social media. Specifically, we develop two methods to cope with different functions of hashtags separately. The first one is named hashtag-generated Topic over Time (hgToT), in which a document is generated jointly by the existing words and hashtags. To enhance the significant effect of hashtags via topic variables, we further develop the second model named hashtag-supervised Topic over Time (hsToT), in which hashtags are treated as useful topic indicators of the tweet. Time information is modeled similarly in both hgToT and hsToT. The proposed two methods are able to capture the hashtags distribution over topics and topic changing over time simultaneously. Experiments on the dataset obtained from Twitter show that both hgToT and hsToT could detect the important information and track the meaningful content and topics successfully. [ABSTRACT FROM AUTHOR]

One of the prevalent studies on Topic Detection and Tracking (TDT) is topic evolution. With the emergence of Internet data, there is a clear need for intuitive and adaptive methods to analyze a series of evolutions. Most approaches completely depend on topic models and only focus on whether topics are changed while ignoring the degree of changes, resulting in poor quality topics and insensitivity of changes. In this paper, we propose a framework of topic evolution based on semantic connections which not only indicates the content similarity between documents but also shows the time decay for an adaptive number of topics and rapid responses to the changes of contents. Additionally, semantic connection features can be used to visualize topic evolution, which makes the analyses much easier. For empirical studies, three data sets in real applications are chosen to prove the effectiveness of our method, and the results show that our method has a better performance in reducing redundant topics, avoiding topic suppression, and discerning the vanishment of old topics and the appearance of new topics. [ABSTRACT FROM AUTHOR]

Traditional relational topic models provide a successful way to discover the hidden topics from a document network. Many theoretical and practical tasks, such as dimensional reduction, document clustering, and link prediction, could benefit from this revealed knowledge. However, existing relational topic models are based on an assumption that the number of hidden topics is known a priori, which is impractical in many real-world applications. Therefore, in order to relax this assumption, we propose a nonparametric relational topic model using stochastic processes instead of fixed-dimensional probability distributions in this paper. Specifically, each document is assigned a Gamma process, which represents the topic interest of this document. Although this method provides an elegant solution, it brings additional challenges when mathematically modeling the inherent network structure of typical document network, i.e., two spatially closer documents tend to have more similar topics. Furthermore, we require that the topics are shared by all the documents. In order to resolve these challenges, we use a subsampling strategy to assign each document a different Gamma process from the global Gamma process, and the subsampling probabilities of documents are assigned with a Markov Random Field constraint that inherits the document network structure. Through the designed posterior inference algorithm, we can discover the hidden topics and its number simultaneously. Experimental results on both synthetic and real-world network datasets demonstrate the capabilities of learning the hidden topics and, more importantly, the number of topics. [ABSTRACT FROM PUBLISHER]

The rapid development of location-based social networks (LBSNs) has provided an unprecedented opportunity for better location-based services through Point-of-Interest (POI) recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper, we propose a context-aware probabilistic matrix factorization method for POI recommendation. Our model is called TGSC-PMF, it exploits textual information, geographical information, social information, categorical information and popularity information, and incorporates these factors effectively. First, we exploit an aggregated Latent Dirichlet Allocation (LDA) model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and then generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, we model the categorical correlations which combine the category bias of users and the popularity of POIs into categorical relevance score. Further, we integrate the interest, geographical, social and categorical relevance scores into probabilistic matrix factorization model (PMF) for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGSC-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques. [ABSTRACT FROM AUTHOR]

Data mining and big data computing are the emerging domains in the current era of predictions for societal applications. Millions of people are interested in sharing their views through tweets. Healthcare predictions are one of the attractive researches in big data social mining. Healthcare predictions are derived by implementing topic models by the ailments data. An ailment refers to either illness or sign of a particular health problem. Millions of tweets are collected based on conditions and assessed with ailment topic aspect models. The existing topic model, Latent Dirichlet Allocation (LDA), Latent Semantic Indexing, Probabilistic LSI (PLSI), limits the healthcare results assessment concerning any one of the ailments aspects. Recent ailments topic aspect model (ATAM) overcome the problems of these topic models and delivers the healthcare assessment results concerning the fundamental aspects of ailments data except side-effects analysis of treatments. The scalability performance of ATAM is degraded in showing healthcare results over the massive amounts of health data. A high-performance computing model of ATAM has been developed in the distributed environment to address scalability. Its intelligent model is designed in the cloud and multi-node Hadoop environment to deliver high-performance social computing results for healthcare. Experiments are conducted on many comparative studies is demonstrated between the existing and proposed high-performance models using the massive amount of health-related tweets concerning the ailments aspects. [ABSTRACT FROM AUTHOR]

Understanding the users' latent intents behind the search queries is critical for search engines. Hence, there has been an increasing attention on studying how to effectively mine the intents of search queries by analyzing search engine query log. However, we observe that the information richness of query log is not fully utilized so far and the information underuse heavily limits the performance of the existing methods. In this paper, we tackle the problem of query intent mining by taking full advantage of the information richness of query log from a multi-dimensional perspective. Specifically, we capture the latent relations between search queries via three different dimensions: the URL dimension, the session dimension and the term dimension. We first propose the Result-Oriented Framework (ROF), which is easy to implement and significantly improves both the precision and the recall of query intent mining. We further propose the Topic-Oriented Framework (TOF), in order to significantly reduce the online time and memory consumptions for query intent mining. TOF employs the Query Log Topic Model (QLTM) that derives the latent topics from query log to integrate the information of the three dimensions in a principled way. The latent topics that are considered as low-dimensional descriptions of the query relations and serve as the basis of efficient online query intent mining. We conduct extensive experiments on a major commercial search engine query log. Experimental results show that the two frameworks significantly outperform the state-of-the-art methods with respect to a variety of metrics. [ABSTRACT FROM AUTHOR]

Many mature term-based or pattern-based approaches have been used in the field of information filtering to generate users’ information needs from a collection of documents. A fundamental assumption for these approaches is that the documents in the collection are all about one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, and this has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering has not been so well explored. Patterns are always thought to be more discriminative than single terms for describing documents. However, the enormous amount of discovered patterns hinder them from being effectively and efficiently used in real applications, therefore, selection of the most discriminative and representative patterns from the huge amount of discovered patterns becomes crucial. To deal with the above mentioned limitations and problems, in this paper, a novel information filtering model, Maximum matched Pattern-based Topic Model (MPBTM), is proposed. The main distinctive features of the proposed model include: (1) user information needs are generated in terms of multiple topics; (2) each topic is represented by patterns; (3) patterns are generated from topic models and are organized in terms of their statistical and taxonomic features; and (4) the most discriminative and representative patterns, called Maximum Matched Patterns, are proposed to estimate the document relevance to the user’s information needs in order to filter out irrelevant documents. Extensive experiments are conducted to evaluate the effectiveness of the proposed model by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models. [ABSTRACT FROM AUTHOR]

In this paper, we present unsupervised language model (LM) adaptation approaches using latent Dirichlet allocation (LDA) and latent semantic marginals (LSM). The LSM is the unigram probability distribution over words that are calculated using LDA-adapted unigram models. The LDA model is used to extract topic information from a training corpus in an unsupervised manner. The LDA model yields a document–topic matrix that describes the number of words assigned to topics for the documents. A hard-clustering method is applied on the document–topic matrix of the LDA model to form topics. An adapted model is created by using a weighted combination of the n -gram topic models. The stand-alone adapted model outperforms the background model. The interpolation of the background model and the adapted model gives further improvement. We modify the above models using the LSM. The LSM is used to form a new adapted model by using the minimum discriminant information (MDI) adaptation approach called unigram scaling, which minimizes the distance between the new adapted model and the other model. The unigram scaling of the adapted model using LSM yields better results over a conventional unigram scaling approach. The unigram scaling of the interpolation of the background and the adapted model using the LSM outperform the background model, the unigram scaling of the background model, the unigram scaling of the adapted model, and the interpolation of the background and the adapted models respectively. We perform experiments using the '87–89 Wall Street Journal (WSJ) corpus incorporating a multi-pass continuous speech recognition (CSR) system. In the first pass, we used the background n -gram language model for lattice generation and then we apply the LM adaptation approaches for lattice rescoring in the second pass. [ABSTRACT FROM AUTHOR]

Probabilistic topic models are statistical methods whose aim is to discover the latent structure in a large collection of documents. The intuition behind topic models is that, by generating documents by latent topics, the word distribution for each topic can be modelled and the prior distribution over the topic learned. In this paper we propose to apply this concept by modelling the topics of sentences for the aspect detection problem in review documents in order to improve sentiment analysis systems. Aspect detection in sentiment analysis helps customers effectively navigate into detailed information about their features of interest. The proposed approach assumes that the aspects of words in a sentence form a Markov chain. The novelty of the model is the extraction of multiword aspects from text data while relaxing the bag-of-words assumption. Experimental results show that the model is indeed able to perform the task significantly better when compared with standard topic models. [ABSTRACT FROM PUBLISHER]

Topic models have been proposed to model a collection of data such as text documents and images in which each object (e.g., a document) contains a set of instances (e.g., words). In many topic models, the dimension of the latent topic space (the number of topics) is assumed to be a deterministic unknown. The number of topics significantly affects the prediction performance and interpretability of the estimated topics. In this paper, we propose a confidence-constrained rank minimization (CRM) to recover the exact number of topics in topic models with theoretical guarantees on recovery probability and mean squared error of the estimation. We provide a computationally-efficient optimization algorithm for the problem to further the applicability of the proposed framework to large real world datasets. Numerical evaluations are used to verify our theoretical results. Additionally, to illustrate the applicability of the proposed framework to practical problems, we provide results in image classification for two real world datasets and text classification for three real world datasets. [ABSTRACT FROM PUBLISHER]

Abstract: Statistical topic models have been proposed for modeling documents and authorship information. However, few previous works have studied the evolution of associated data. In this paper, we investigate how to model trends of changes in document content and author interests simultaneously over time. We propose two models: a bag-of-words based Author–Time–Topic model that extends the state-of-the-art LDA-style topic model and a Hidden Markov Author–Time–Topic model, which can model interdependencies between topics. We use the Gibbs EM algorithm for parameter estimation. We apply these models to two data sets: NIPS papers and Yahoo group posts. Experimental results show that our models can achieve a lower perplexity (−2.0%–20%) than the baseline LDA and Author–Topic model, when modeling quickly evolving associated data. Experiments also reveal that the proposed models can accurately capture the hot topics in different periods (e.g. “Yao at preseason” in Aug-2004, when the Chinese player Ming Yao became a highlight in the NBA) from the two data sets. [Copyright &y& Elsevier]

Nowadays, social network sites (SNSs) have been significant platforms for content sharing in our daily life. With the emergence of different kinds of social network sites and users' diverse needs for content sharing, their content sharing practices are generally taken place in multiple SNSs. To construct models that can characterize users' content sharing practices in a composite context constituted by multiple social network sites (cross-site user generated content modeling) has been an emerging research topic in web data mining and human behavior research. However, previous methods such as Dirichlet Multinomial Mixture model (DMM), Biterm Topic Model (BTM), Twitter-LDA and MultiLDA have limited representation ability or are based on unreliable assumption, which cannot characterize the user generated content (UGC) accurately from the perspective of multiple SNSs. In this paper, we first conduct an empirical study to investigate the characteristics of users' content sharing practices in cross-site context, based on which we propose a more reliable cross-site UGC model named CrossSite-LDA (C-LDA). We then evaluate the performances of the C-LDA model with four state-of-the-art models based on the two data sets sampled from Weibo–Douban and Facebook–Twitter. Results show that the C-LDA has better performances in perplexity, word coherence, topic KL divergence, UCI and UMass metrics compared with existing models, which suggests its superior accuracy on modeling users' content characteristics in cross-site context. [ABSTRACT FROM AUTHOR]

Geoparser is a fundamental component of a Geographic Information Retrieval (GIR) geoparser, which performs toponym recognition, disambiguation, and geographic coordinate resolution from unstructured text domain. However, geoparsing of news articles which report several events across many place-mentions in the document are not yet adequately handled by regular geoparser, where the scope of resolution is either toponym-level or document-level. The capacity to detect multiple events and geolocate their true coordinates along with their numerical arguments is still missing from modern geoparsers, much less in Indonesian news corpora domain. We propose an event geoparser model with three stages of processing, which tightly integrates event extraction model into geoparsing and provides precise event-level resolution scope. The model casts the geotagging and event extraction as sequence labeling and uses LSTM-CRF inferencer equipped with features derived using Aggregated Topic Model from a large corpus to increase the generalizability. Throughout the proposed workflow and features, the geoparser is able to significantly improve the identification of pseudo-location entities, resulting in a 23.43% increase for weighted F1 score compared to baseline gazetteer and POS Tag features. As a side effect of event extraction, various numerical arguments are also extracted, and the output is easily projected to a rich choropleth map from a single news document. [ABSTRACT FROM AUTHOR]

Latent Dirichlet allocation (LDA) is a popular algorithm for discovering semantic structure in large collections of text or other data. Although its complexity is linear in the data size, its use on increasingly massive collections has created considerable interest in parallel implementations. “Approximate distributed” LDA, or AD-LDA, approximates the popular collapsed Gibbs sampling algorithm for LDA models while running on a distributed architecture. Although this algorithm often appears to perform well in practice, its quality is not well understood theoretically or easily assessed on new data. In this work, we theoretically justify the approximation, and modify AD-LDA to track an error bound on performance. Specifically, we upper bound the probability of making a sampling error at each step of the algorithm (compared to an exact, sequential Gibbs sampler), given the samples drawn thus far. We show empirically that our bound is sufficiently tight to give a meaningful and intuitive measure of approximation error in AD-LDA, allowing the user to track the tradeoff between accuracy and efficiency while executing in parallel. [ABSTRACT FROM PUBLISHER]

Time stamped texts, or text sequences, are ubiquitous in real-world applications. Multiple text sequences are often related to each other by sharing common topics. The correlation among these sequences provides more meaningful and comprehensive clues for topic mining than those from each individual sequence. However, it is nontrivial to explore the correlation with the existence of asynchronism among multiple sequences, i.e., documents from different sequences about the same topic may have different time stamps. In this paper, we formally address this problem and put forward a novel algorithm based on the generative topic model. Our algorithm consists of two alternate steps: the first step extracts common topics from multiple sequences based on the adjusted time stamps provided by the second step; the second step adjusts the time stamps of the documents according to the time distribution of the topics discovered by the first step. We perform these two steps alternately and after iterations a monotonic convergence of our objective function can be guaranteed. The effectiveness and advantage of our approach were justified through extensive empirical studies on two real data sets consisting of six research paper repositories and two news article feeds, respectively. [ABSTRACT FROM AUTHOR]