ERD-MedLDA: Entity relation detection using supervised topic models with maximum margin learning

2012 ◽  
Vol 18 (2) ◽  
pp. 263-289 ◽  
Author(s):  
DINGCHENG LI ◽  
SWAPNA SOMASUNDARAN ◽  
AMIT CHAKRABORTY
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Topic Models ◽  
Likelihood Estimation ◽  
Named Entities ◽  
Maximum Margin ◽  
Heterogeneous Features ◽  
Overall Performance ◽  
Entity Relation Detection

AbstractThis paper proposes a novel application of topic models to do entity relation detection (ERD). In order to make use of the latent semantics of text, we formulate the task of relation detection as a topic modeling problem. The motivation is to find underlying topics that are indicative of relations between named entities (NEs). Our approach considers pairs of NEs and features associated with them as mini documents, and aims to utilize the underlying topic distributions as indicators for the types of relations that may exist between the NE pair. Our system, ERD-MedLDA, adapts Maximum Entropy Discriminant Latent Dirichlet Allocation (MedLDA) with mixed membership for relation detection. By using supervision, ERD-MedLDA is able to learn topic distributions indicative of relation types. Further, ERD-MedLDA is a topic model that combines the benefits of both, maximum likelihood estimation (MLE) and maximum margin estimation (MME), and the mixed-membership formulation enables the system to incorporate heterogeneous features. We incorporate different features into the system and perform experiments on the ACE 2005 corpus. Our approach achieves better overall performance for precision, recall, and F-measure metrics as compared to baseline SVM-based and LDA-based models. We also find that our system shows better and consistent improvements with the addition of complex informative features as compared to baseline systems.

scholarly journals Topic Modeling in Embedding Spaces

2020 ◽  
Vol 8 ◽  
pp. 439-453 ◽  
Author(s):  
Adji B. Dieng ◽  
Francisco J. R. Ruiz ◽  
David M. Blei
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Topic Models ◽  
Predictive Performance ◽  
Inner Product ◽  
Natural Parameter ◽  
Document Models ◽  
Heavy Tailed ◽  
Categorical Distribution

Topic modeling analyzes documents to learn meaningful patterns of words. However, existing topic models fail to learn interpretable topics when working with large and heavy-tailed vocabularies. To this end, we develop the embedded topic model (etm), a generative model of documents that marries traditional topic models with word embeddings. More specifically, the etm models each word with a categorical distribution whose natural parameter is the inner product between the word’s embedding and an embedding of its assigned topic. To fit the etm, we develop an efficient amortized variational inference algorithm. The etm discovers interpretable topics even with large vocabularies that include rare words and stop words. It outperforms existing document models, such as latent Dirichlet allocation, in terms of both topic quality and predictive performance.

Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation

2018 ◽  
Vol 18 (1) ◽  
pp. 101-117 ◽  
Author(s):  
Carlo Schwarz
Keyword(s):  
Machine Learning ◽  
Probability Distribution ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Topic Models ◽  
Text Documents ◽  
Text Data ◽  
Dirichlet Allocation

In this article, I introduce the ldagibbs command, which implements latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most popular machine-learning topic model. Topic models automatically cluster text documents into a user-chosen number of topics. Latent Dirichlet allocation represents each document as a probability distribution over topics and represents each topic as a probability distribution over words. Therefore, latent Dirichlet allocation provides a way to analyze the content of large unclassified text data and an alternative to predefined document classifications.

scholarly journals Learning Topic Models by Neighborhood Aggregation

2019 ◽  
Author(s):  
Ryohei Hisano
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
State Of The Art ◽  
Topic Models ◽  
Word Embedding ◽  
Output Function ◽  
Inference Procedure ◽  
Simple Manner ◽  
Classification Tasks

Topic models are frequently used in machine learning owing to their high interpretability and modular structure. However, extending a topic model to include a supervisory signal, to incorporate pre-trained word embedding vectors and to include a nonlinear output function is not an easy task because one has to resort to a highly intricate approximate inference procedure. The present paper shows that topic modeling with pre-trained word embedding vectors can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words. From the network view of topic models, nodes correspond to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing the same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and include a nonlinear output function in a simple manner. In experiments, we show that our approach outperforms the state-of-the-art supervised Latent Dirichlet Allocation implementation in terms of held-out document classification tasks.

scholarly journals Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 376 ◽  
Author(s):  
Cornelia Ferner ◽  
Clemens Havas ◽  
Elisabeth Birnbacher ◽  
Stefan Wegenkittl ◽  
Bernd Resch
Keyword(s):  
Event Detection ◽  
Disaster Response ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Geographic Area ◽  
Relevant Information ◽  
Suggested Approach ◽  
Napa Valley ◽  
Source Of Information

In the event of a natural disaster, geo-tagged Tweets are an immediate source of information for locating casualties and damages, and for supporting disaster management. Topic modeling can help in detecting disaster-related Tweets in the noisy Twitter stream in an unsupervised manner. However, the results of topic models are difficult to interpret and require manual identification of one or more “disaster topics”. Immediate disaster response would benefit from a fully automated process for interpreting the modeled topics and extracting disaster relevant information. Initializing the topic model with a set of seed words already allows to directly identify the corresponding disaster topic. In order to enable an automated end-to-end process, we automatically generate seed words using older Tweets from the same geographic area. The results of two past events (Napa Valley earthquake 2014 and hurricane Harvey 2017) show that the geospatial distribution of Tweets identified as disaster related conforms with the officially released disaster footprints. The suggested approach is applicable when there is a single topic of interest and comparative data available.

scholarly journals A Method for Constructing Supervised Topic Model Based on Term Frequency-Inverse Topic Frequency

Symmetry ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1486
Author(s):  
Zhinan Gou ◽  
Zheng Huo ◽  
Yuanzhen Liu ◽  
Yi Yang
Keyword(s):  
Topic Modeling ◽  
Topic Model ◽  
State Of The Art ◽  
Topic Models ◽  
Document Classification ◽  
Experimental Results ◽  
Tag Recommendation ◽  
Term Frequency ◽  
Series Of Experiments ◽  
Dirichlet Prior

Supervised topic modeling has been successfully applied in the fields of document classification and tag recommendation in recent years. However, most existing models neglect the fact that topic terms have the ability to distinguish topics. In this paper, we propose a term frequency-inverse topic frequency (TF-ITF) method for constructing a supervised topic model, in which the weight of each topic term indicates the ability to distinguish topics. We conduct a series of experiments with not only the symmetric Dirichlet prior parameters but also the asymmetric Dirichlet prior parameters. Experimental results demonstrate that the result of introducing TF-ITF into a supervised topic model outperforms several state-of-the-art supervised topic models.

scholarly journals CompareLDA: A Topic Model for Document Comparison

2019 ◽  
Vol 33 ◽  
pp. 7112-7119
Author(s):  
Maksim Tkachenko ◽  
Hady W. Lauw
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Estimation Method ◽  
Pairwise Comparison ◽  
Likelihood Estimation ◽  
Variational Approximation ◽  
Textual Representations ◽  
Real World Applications ◽  
Public Datasets ◽  
Maximum Likelihood Estimation Method

A number of real-world applications require comparison of entities based on their textual representations. In this work, we develop a topic model supervised by pairwise comparisons of documents. Such a model seeks to yield topics that help to differentiate entities along some dimension of interest, which may vary from one application to another. While previous supervised topic models consider document labels in an independent and pointwise manner, our proposed Comparative Latent Dirichlet Allocation (CompareLDA) learns predictive topic distributions that comply with the pairwise comparison observations. To fit the model, we derive a maximum likelihood estimation method via augmented variational approximation algorithm. Evaluation on several public datasets underscores the strengths of CompareLDA in modelling document comparisons.

Can topic models be used in research evaluations? Reproducibility, validity, and reliability when compared with semantic maps

2019 ◽  
Vol 28 (3) ◽  
pp. 263-272 ◽  
Author(s):  
Tobias Hecking ◽  
Loet Leydesdorff
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Research Evaluation ◽  
Topic Models ◽  
Principal Component ◽  
Evaluation Framework ◽  
Validity And Reliability ◽  
Text Corpora ◽  
Semantic Maps ◽  
Semantic Coherence

AbstractWe replicate and analyze the topic model which was commissioned to King’s College and Digital Science for the Research Evaluation Framework (REF 2014) in the United Kingdom: 6,638 case descriptions of societal impact were submitted by 154 higher-education institutes. We compare the Latent Dirichlet Allocation (LDA) model with Principal Component Analysis (PCA) of document-term matrices using the same data. Since topic models are almost by definition applied to text corpora which are too large to read, validation of the results of these models is hardly possible; furthermore the models are irreproducible for a number of reasons. However, removing a small fraction of the documents from the sample—a test for reliability—has on average a larger impact in terms of decay on LDA than on PCA-based models. The semantic coherence of LDA models outperforms PCA-based models. In our opinion, results of the topic models are statistical and should not be used for grant selections and micro decision-making about research without follow-up using domain-specific semantic maps.

Incorporating Biterm Correlation Knowledge into Topic Modeling for Short Texts

2020 ◽  
Author(s):  
Kai Zhang ◽  
Yuan Zhou ◽  
Zheng Chen ◽  
Yufei Liu ◽  
Zhuo Tang ◽  
...  
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Semantic Knowledge ◽  
Superior Performance ◽  
Knowledge Based ◽  
Modeling Process ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Latent Topic

Abstract The prevalence of short texts on the Web has made mining the latent topic structures of short texts a critical and fundamental task for many applications. However, due to the lack of word co-occurrence information induced by the content sparsity of short texts, it is challenging for traditional topic models like latent Dirichlet allocation (LDA) to extract coherent topic structures on short texts. Incorporating external semantic knowledge into the topic modeling process is an effective strategy to improve the coherence of inferred topics. In this paper, we develop a novel topic model—called biterm correlation knowledge-based topic model (BCK-TM)—to infer latent topics from short texts. Specifically, the proposed model mines biterm correlation knowledge automatically based on recent progress in word embedding, which can represent semantic information of words in a continuous vector space. To incorporate external knowledge, a knowledge incorporation mechanism is designed over the latent topic layer to regularize the topic assignment of each biterm during the topic sampling process. Experimental results on three public benchmark datasets illustrate the superior performance of the proposed approach over several state-of-the-art baseline models.

scholarly journals CLDA: An Effective Topic Model for Mining User Interest Preference under Big Data Background

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Lirong Qiu ◽  
Jia Yu
Keyword(s):  
Big Data ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
User Interest ◽  
Text Data ◽  
Data Set ◽  
Data Sparsity ◽  
Short Text ◽  
Text Filtering

In the present big data background, how to effectively excavate useful information is the problem that big data is facing now. The purpose of this study is to construct a more effective method of mining interest preferences of users in a particular field in the context of today’s big data. We mainly use a large number of user text data from microblog to study. LDA is an effective method of text mining, but it will not play a very good role in applying LDA directly to a large number of short texts in microblog. In today’s more effective topic modeling project, short texts need to be aggregated into long texts to avoid data sparsity. However, aggregated short texts are mixed with a lot of noise, reducing the accuracy of mining the user’s interest preferences. In this paper, we propose Combining Latent Dirichlet Allocation (CLDA), a new topic model that can learn the potential topics of microblog short texts and long texts simultaneously. The data sparsity of short texts is avoided by aggregating long texts to assist in learning short texts. Short text filtering long text is reused to improve mining accuracy, making long texts and short texts effectively combined. Experimental results in a real microblog data set show that CLDA outperforms many advanced models in mining user interest, and we also confirm that CLDA also has good performance in recommending systems.

Detecting the association of health problems in consumer-level medical text

2016 ◽  
Vol 44 (1) ◽  
pp. 3-14
Author(s):  
Chong Chen ◽  
Edgar Huang ◽  
Hongfei Yan
Keyword(s):  
Concept Mapping ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Complete Information ◽  
Health Problems ◽  
Medical Subject Headings ◽  
Medical Text ◽  
Expert Rating ◽  
Overall Performance ◽  
Expert Ratings

Consumers usually do not know the complicated links between related health problems. This fact may cause troubles when they wish to seek complete information regarding such problems. This study detects the associations among health problems by extending the meaning of health terms with methods based on the latent Dirichlet allocation (LDA) probability topic model, the Medical Subject Headings (MeSH) thesaurus structure and the Wikipedia concept mapping. The terms represented health problems are selected from and extended by the consumer-level medical text. The vocabulary is different between the consumer-level and the professional-level medical text. Thus, the findings can be easily understood by the general public and be suitable to consumer-oriented applications. The methods were evaluated in two ways: (1) correlation analysis with expert rating to show the overall performance and (2) P@ N to reflect the ability of detecting strong associations. The LDA topic-model-based method outperforms the other two types. The judgment incongruence between the best method and the expert ratings has been examined, and the evidence shows that the automatic method sometimes detects real associations beyond those identified by human experts.

Sign in / Sign up

Export Citation Format

Share Document