Unsupervised Topic Model Based Text Network Construction for Learning Word Embeddings

Objective: The objective is to develop an ensemble of machine learning algorithms to identify multilingual, online articles that are relevant to biosurveillance. Language morphology varies widely across languages and must be accounted for when designing algorithms. Here, we compare the performance of a word embedding-based approach and a topic modeling approach with machine learning algorithms to determine the best method for Chinese, Arabic, and French languages.Introduction: Global biosurveillance is an extremely important, yet challenging task. One form of global biosurveillance comes from harvesting open source online data (e.g. news, blogs, reports, RSS feeds). The information derived from this data can be used for timely detection and identification of biological threats all over the world. However, the more inclusive the data harvesting procedure is to ensure that all potentially relevant articles are collected, the more data that is irrelevant also gets harvested. This issue can become even more complex when the online data is in a non-native language. Foreign language articles not only create language-specific issues for Natural Language Processing (NLP), but also add a significant amount of translation costs. Previous work shows success in the use of combinatory monolingual classifiers in specific applications, e.g., legal domain [1]. A critical component for a comprehensive, online harvesting biosurveillance system is the capability to identify relevant foreign language articles from irrelevant ones based on the initial article information collected, without the additional cost of full text retrieval and translation.Methods: The analysis text dataset contains the title and brief description of 3506 online articles in Chinese, Arabic, and French languages from the date range of August, 17, 2016 to July 5, 2017. The NLP article pre-processing steps are language-specific tokenization and stop words removal. We compare two different approaches: word embeddings and topic modeling (Fig. 1). For word embeddings, we first generate word vectors for the data using a pretrained Word2Vec (W2V) model [2]. Subsequently, the word vectors within a document are averaged to produce a single feature vector for the document. Then, we fit a machine learning algorithm (random forest classifier or Support Vector Machine (SVM)) to the training vectors and get predictions for the test documents. For topic modelling, we used a Latent Dirichlet Allocation (LDA) model to generate five topics for all relevant documents [3]. For each new document, the output is the probability measure for the document belonging to these five topics. Here, we classify the new document by comparing the probability measure with a relevancy threshold.Results: The Word2Vec model combined with a random forest classifier outperformed the other approaches across the three languages (Fig. 2); the Chinese model has an 89% F1-score, the Arabic model has 86%, and the French model has 94%. To decrease the chance of calling a potentially relevant article irrelevant, high recall was more important than high precision. In the Chinese model, the Word2Vec with a random forest approach had the highest recall at 98% (Table 1).Conclusions: We present research findings on different approaches of relevance to biosurveillance identification on non-English texts and identify the best performing methods for implementation into a biosurveillance online article harvesting system. Our initial results suggest that the word embeddings model has an advantage over topic modeling, and the random forest classifier outperforms the SVM. Directions for future work will aim to further expand the list of languages and methods to be compared, e.g., n-grams and non-negative matrix factorization. In addition, we will fine-tune the Arabic and French model for better accuracy results.

A massive of text that is generated every minute is increasing dramatically. Therefore, it is more and more important to find an effective model to automatic classify the amount of text. Topic models is the most powerful techniques in text classification. There are many research results in the field of topic model have been published in scholarly journals. The Latent Dirichlet Allocation (LDA) is one of the most popular topic models in text classification. Researchers have proposed many topic evolution models based on LDA to solve some specific problems in applications of text classification. And some joint models which based on topic models combined other algorithms have been studied to enhance the performance of text classification. In this paper, we investigated three categories topic models for text classification and briefly introduced their advantages and disadvantages in the applications of text mining. Also, we introduce the generated process of documents and illustrate the graphical model for each topic models.

Representation Learning and Learning from Limited Labeled Data for Community Question Answering

Integrating Big Data Into Evaluation: R Code for Topic Identification and Modeling

Instructional design and technology (IDT) professionals participate in communities of practice (CoPs) on Facebook to seek pedagogical and educational technology advice for solving instructional design (ID) problems. Much of the IDT literature has focused on formal educational environments and not on nonformal settings outside the classroom and beyond formal education. Further analysis of tacit or practical knowledge exchanged among community members is required to understand the purpose, functions, and organizational knowledge capital in online CoPs. To fill this gap, this study uses natural language processing (NLP) to analyze the practical knowledge of 6,066 anonymized users’ posts from four large public IDT CoPs on Facebook from September 2017 to September 2020 after cleaning the dataset. User posts were publicly available and required no password authentication for access, including Instructional Designer (4,717), Designers for Learning (228), Adobe Captivate Users (599), and Articulate Storyline (522). The proposed methodology aims to extract practical knowledge of individual online CoPs in three parts. First, the characteristics of written communication among members are extracted by calculating word and sentence lengths, word frequencies, and contiguous words. Second, the characteristics of members’ exchange of practical knowledge are obtained through sentiment identification, entity recognition, and relationships between pedagogical and educational technology entities. Third, the functions of individual online CoPs are developed through topic modeling with latent Dirichlet allocation (LDA) and BERTopic. The findings suggest similarities and differences among IDT CoPs, different resource distribution conventions, and members exchanging pedagogical and educational technology advice. The study highlights the need for pedagogical foundations to support instructional and technical decisions, mechanisms for self-assessment of practical knowledge concerning IDT competencies, community protocols for addressing misconceptions about learning, onboarding materials for new members, and new topic structures to classify practical knowledge. NLP tasks are implemented using Python libraries to support the future development of awareness tools.

Topic modelling is an important technique for extracting meaningful insights from large volumes of unstructured text data. The paper presents a federated technique for topic modelling that is based on a novel approach of the Latent Dirichlet Allocation (LDA) method for topic model generation. The proposed approach enables a topic model to be developed in a distributed environment using data continually generated from multiple sources without the need for sharing actual data. The first iteration of the topic modelling uses unsupervised LDA at each device generating the data. The results of each device are aggregated at a central server to generate a set of seed words that are used for guided LDA by the subsequent iterations of topic modelling. The proposed work, Federated LDA (F-LDA) has been evaluated using two datasets: a text dataset of dialogues between patients and doctors based on factual conversations and another comprising tweets related to depression. Comparing the performance of F-LDA with that of a centralized LDA, it was observed that F-LDA results in improved coherence score as well as diversity score in comparison to centralized LDA. This indicates that F-LDA achieves better interpretable topics covering a wide range of themes without redundancy.

As text data continues to grow quickly, it is increasingly important to develop intelligent systems to help people manage and make use of vast amounts of text data (big text data''). As a new family of effective general approaches to text data retrieval and analysis, probabilistic topic models---notably Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocations (LDA), and their many extensions---have been studied actively in the past decade with widespread applications. These topic models are powerful tools for extracting and analyzing latent topics contained in text data; they also provide a general and robust latent semantic representation of text data, thus improving many applications in information retrieval and text mining. Since they are general and robust, they can be applied to text data in any natural language and about any topics. This tutorial systematically reviews the major research progress in probabilistic topic models and discuss their applications in text retrieval and text mining. The tutorial provides (1) an in-depth explanation of the basic concepts, underlying principles, and the two basic topic models (i.e., PLSA and LDA) that have widespread applications, (2) an introduction to EM algorithms and Bayesian inference algorithms for topic models, (3) a hands-on exercise to allow the tutorial attendants to learn how to use the topic models implemented in the MeTA Open Source Toolkit and experiment with provided data sets, (4) a broad overview of all the major representative topic models that extend PLSA or LDA, and (5) a discussion of major challenges and future research directions.

Topic modeling extracts useful potential topics that reflect market information from massive financial news and is widely used in data mining and economic research. Traditional topic modeling approaches such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) lack semantic information, and short texts have feature sparse problems. We develop a topic clustering model based on BERT-LDA joint embedding that takes both contextual semantics and thematic narrative into account. We cluster document embeddings with the HDBSCAN algorithm and utilize a class-based TF-IDF (c-TF-IDF) method to create topic representations. Empirical results show that the BERT-LDA model is competitive compared with traditional and single topic models. It generates coherent topic words that are dissimilar to each other.

Word embeddings are finding their increasing application in a variety of biomedical Natural Language Processing (bioNLP) tasks, ranging from drug discovery to automated disease diagnosis. While these word embeddings in their entirety have shown meaningful syntactic and semantic regularities, however, the meaning of individual dimensions remains elusive. This becomes problematic both in general and particularly in sensitive domains such as bio-medicine, wherein, the interpretability of results is crucial to its widespread adoption. To address this issue, in this study, we aim to improve the interpretability of pre-trained word embeddings generated from a text corpora, and in doing so provide a systematic approach to formalize the problem. More specifically, we exploit the rich categorical knowledge present in the biomedical domain, and propose to learn a transformation matrix that transforms the input embeddings to a new space where they are both interpretable and retain their original expressive features. Experiments conducted on the largest available biomedical corpus suggests that the model is capable of performing interpretability that resembles closely to the human-level intuition.

Analysis of Consumer buying patterns is crucial for vendors to understand the necessities and favorites of their audience. With the increasing availability of digital data, collecting large amounts of consumer feedback and opinions has become easier. However, analyzing such data is challenging due to the presence of unstructured text data. Latent Dirichlet Allocation (LDA) is a prevalent topic modeling procedure that can be used to analyze unstructured text data. LDA allows for the identification of latent topics that are present in the data, enabling businesses to gain insights into consumer behavior. By applying LDA to consumer feedback data, businesses can identify the key topics that are most important to their customers. Additionally, LDA can be used to track changes in consumer preferences over time, allowing businesses to stay up-to-date with evolving market trends. Overall, LDA is a powerful tool for analyzing consumer behavior and can provide valuable insights for businesses looking to improve their products and services. In this work, LDA is employed to analyze consumer tweets using topic modeling. LDA has successfully generated topic and

In an era dominated by an unprecedented deluge of textual information, the need for effective methods to make sense of large datasets is more pressing than ever. This article takes a pragmatic approach to unraveling the intricacies of topic modeling, with a specific focus on the widely used Latent Dirichlet Allocation (LDA) algorithm. The initial segment of the article lays the groundwork by exploring the practical relevance of topic modeling in real-world scenarios. It addresses the everyday challenges faced by researchers and professionals dealing with vast amounts of unstructured text, emphasizing the potential of topic modeling to distill meaningful insights from seemingly chaotic data. Moving beyond theoretical abstraction, the article then delves into the mechanics of Latent Dirichlet Allocation. Developed in 2003 by Blei, Ng, and Jordan, LDA provides a probabilistic framework to identify latent topics within documents. The article takes a step-by-step approach to demystify LDA, offering a practical understanding of its components and the Bayesian principles governing its operation. A significant portion of the article is dedicated to the practical implementation of LDA. It provides insights into preprocessing steps, parameter tuning, and model evaluation, offering readers a hands-on guide to applying LDA in their own projects. Real-world examples and case studies showcase how LDA can be a valuable tool for tasks such as document clustering, topic summarization, and sentiment analysis. However, the journey through LDA is not without challenges, and the article candidly addresses these hurdles. Topics such as determining the optimal number of topics, the sensitivity of results to parameter settings, and the interpretability of outcomes are discussed. This realistic appraisal adds depth to the article, helping readers navigate the nuances and potential pitfalls of employing LDA in practice. Beyond the technical intricacies, the article explores the broad spectrum of applications where LDA has proven its efficacy. From text mining and information retrieval to social network analysis and healthcare informatics, LDA has left an indelible mark on diverse domains. Through practical examples, the article illustrates how LDA can be adapted to different contexts, showcasing its versatility as a tool for uncovering latent patterns. Keywords: Topic Modeling, Latent Dirichlet Allocation, Text Mining, Natural Language Processing, Document Clustering, Bayesian Inference.

Topic modeling is the statistical model for discovering hidden topics or keywords in a collection of documents. Topic modeling is also considered a probabilistic model for learning, analyzing, and discovering topics from the document collection. The most popular techniques for topic modeling are latent semantic analysis (LSA), probabilistic latent semantic analysis (pLSA), latent Dirichlet allocation (LDA), and the recent deep learning-based lda2vec. LDA is most commonly used in extractive multi-document summarization to determine whether the extracted sentence reflects the concept of the input document. In this paper, we will try to explore various multi-document summarization techniques that use LDA as a topic modeling method for improving final summary coverage and to reduce redundancy. Finally, we compared LDA and LSA using the Genism toolkit, and our experiment results show that LDA outperforms LSA if we increase the number of features considered for sentence selection.KeywordsTopic modelingText summarizationLDALSA

Word embeddings, distributed word representations in a reduced linear space, show a lot of promise for accomplishing Natural Language Processing (NLP) tasks in an unsupervised manner. In this study, we investigate if the success of word2vec, a Neural Networks based word embeddings algorithm, can be replicated in an aggluginative language like Turkish. Turkish is more challenging than languages like English for complex NLP tasks because of her rich morphology. We picked ontology enrichment, again a relatively harder NLP task, as our test application. Firstly, we show how ontological relations can be extracted automaticaly from Turkish Wikipedia to construct a gold standard. Then by running experiments we show that the word vector representations produced by word2vec are useful to detect ontological relations encoded in Wikipedia. We propose a simple but yet effective weakly supervised ontology enrichment algorithm where for a given word a few know ontologically related concepts coupled with similarity scores computed via word2vec models can result in discovery of other related concepts. We argue how our algorithm can be improved and augmented to make it a viable component of an ontoloy learning and population framework.

Learning the inherent meaning of a word in Natural Language Processing (NLP) has motivated researchers to represent a word at various levels of abstraction, namely character-level, morpheme-level, and subword-level vector representations. Syllable-Aware Word Embedding (SAWE) can effectively handle agglutinative and fusion-based NLP tasks. However, research attempts on assessing the SAWE on such extrinsic NLP tasks has been scanty, especially for low-resource languages in the context of code-mixing with English. A model to learn SAWE to extract semantics at fine-grained subunits of a word is proposed in this article, and the representative ability of the embeddings is assessed through sentiment analysis of code-mixed Telugu-English review corpora. Multilingual societies and advancements in communication technologies have accounted for the prolific usage of mixed data, which renders the State-of-the-Art (SOTA) sentiment analysis models developed based on monolingual data ineffective. Social media users in the Indian subcontinent exhibit a tendency to mix English and their respective native language (using the phonetic form of English) in expressing their opinions or sentiments. A code-mixing scenario provides flexibility to borrow words from a foreign language, usage of shorthand notations, elongation of vowels, and usage of words without following syntactic/grammatical rules, which renders the sentiment analysis of code-mixed data challenging to perform. Deep neural architectures like Long Short-Term Memory and Gated Recurrent Unit networks have been shown to be effective in solving several NLP tasks, such as sequence labeling, named entity recognition, and machine translation. In this article, a framework to perform sentiment analysis on a code-mixed Telugu-English review corpus is implemented. Both word embedding and SAWE are input to a unified deep neural network that contains a two-level Bidirectional Long Short-Term Memory/Gated Recurrent Unit network with Softmax as the output layer. The proposed model leverages the advantages of both word embedding and SAWE, which enable the proposed model to outperform existing SOTA code-mixed sentiment analysis models on a Telugu-English code-mixed dataset of the International Institute of Information Technology–Hyderabad and a dataset curated by the authors. The improvement realized by the proposed model on these datasets is [3% increase in F1-score and 2% increase in accuracy] and [7% increase in F1-score and 5% in accuracy], respectively, in comparison with the best-performing SOTA model.

Topic models have recently shown to be really useful tools for the analysis of microarray experiments. In particular they have been successfully applied to gene clustering and, very recently, also to samples classification. In this latter case, nevertheless, the basic assumption of functional independence between genes is limiting, since many other a priori information about genes’ interactions may be available (co-regulation, spatial proximity or other a priori knowledge). In this paper a novel topic model is proposed, which enriches and extends the Latent Dirichlet Allocation (LDA) model by integrating such dependencies, encoded in a categorization of genes. The proposed topic model is used to derive a highly informative and discriminant representation for microarray experiments. Its usefulness, in comparison with standard topic models, has been demonstrated in two different classification tests.KeywordsTopic ModelLatent Dirichlet AllocationMultinomial DistributionProbabilistic Latent Semantic AnalysisLatent Dirichlet Allocation ModelThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.