[PDF] Top 20 Efficient Methods for Incorporating Knowledge into Topic Models
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Efficient Methods for Incorporating Knowledge into Topic Models
... sampling methods (Yao et ...of topic modeling to search engines and online advertising, where cap- turing the “long tail” of infrequently used topics requires large topic ...LDA models in ... See full document
10
Efficient Methods for Inferring Large Sparse Topic Hierarchies
... flat topic models is found in recent hierarchical topic models (Paisley et ...the topic of the Chicago Bulls is also relevant to the more general topics of NBA, Basketball, and ...Bulls ... See full document
11
Incorporating Word Correlation Knowledge into Topic Modeling
... correlation knowledge to improve the coherence of topic ...Existing topic models assume words are generated in- dependently and lack the mechanism to utilize the rich similarity relationships ... See full document
10
Online Bayesian Passive-Aggressive Learning
... of models. Despite its general intractability, efficient algorithms have been proposed under different ...deriving efficient Monte Carlo methods without making strict ...relational ... See full document
39
Authorship Attribution with Topic Models
... DADT-P’s testing result is comparable to the third-best accuracy (out of 17) obtained in the PAN’11 competition (Argamon and Juola 2011) (competitors were ranked accord- ing to macro-averaged and micro-averaged ... See full document
42
Discovering Latent Structure in Task Oriented Dialogues
... “ac- knowledge agent” and “resolve problem”, since their underlying language models are likely to pro- duce similar probability distributions over ...By incorporating topic information, our ... See full document
11
PROPOSED MODELS OF ADAPTIVE KNOWLEDGE AGGREGATOR
... right knowledge contributes to better decision making and thus, improves competitiveness and organizational ...their knowledge properly through knowledge management processes as to sustain in the ... See full document
8
Most Trending Topics with Pre-learned Knowledge in Twitter
... domain knowledge in the form of must-links ...these models are based on one assumption that the knowledge introduced is correct, and it can also be easily ...prior knowledge. In this paper, we ... See full document
9
Title: KNOWLEDGE MODELS FRAMEWORK USING TOPIC MAP
... the topic maps. Topic Maps are a tool to organize information in a way that is optimized for ...need. Topic Maps are the online equivalent of printed indexes, and it happens that they can do more: ... See full document
6
TSDPMM: Incorporating Prior Topic Knowledge into Dirichlet Process Mixture Models for Text Clustering
... Our work is related to papers (Vlachos et al., 2008; Vlachos et al., 2009), which added supervi- sion (instance-level must-links or cannot-links be- tween documents) to the DPMM. (Ahmed and X- ing, 2008) proposed ... See full document
6
Topic Models and Fusion Methods: a Union to Improve Text Clustering and Cluster Labeling
... In this paper, we propose a method to enrich document representation vectors to be used in partitional text clustering and cluster labeling. Our method is an unsupervised approach, needless of any external ... See full document
7
Incorporating Lexical Priors into Topic Models
... the models are combined, the performance improves over each of them and is also better than the baseline ...dividual models improve both the topic-word and document-topic distributions ...the ... See full document
10
Efficient Learning for Undirected Topic Models
... Replicated Softmax model (RSM) (Hinton and Salakhutdinov, 2009), a kind of typical undirected topic model, is composed of a family of Restricted Boltzmann Machines (RBMs). Commonly, RSM is learned like standard ... See full document
6
Shared Components Topic Models
... Unlike the SCTM and SAGE, most prior exten- sions to LDA have enhanced the distribution over topics for each document. One of the closest is hier- archical LDA (hLDA) (Blei et al., 2004) and its ap- plication to PAM ... See full document
10
Bayesian Checking for Topic Models
... Political blogs. The CMU 2008 political blog cor- pus consists of six blogs, three of which supported Barack Obama and three of which supported John McCain. This corpus has previously been consid- ered in the context of ... See full document
11
Statistical Models for Topic Segmentation
... To reiterate, we used our word frequency model with a total of 3 parameters trained from English newswire text to segment Spanish broadcast news data.. regions of a document unless those[r] ... See full document
8
Automatic Labelling of Topic Models
... With topic modelling, however, the top-ranking topic terms tended to be associated and not lexically similar to one ...to topic mod- els, but it would certainly be interesting to investi- gate ... See full document
10
Distributed Algorithms for Topic Models
... widely-used topic models, namely the Latent Dirichlet Allocation (LDA) model, and the Hierarchical Dirichet Process (HDP) ...learning topic models for two multi-million document collections ... See full document
28
Efficient Tree Based Topic Modeling
... Topic modeling with a tree-based prior has been used for a variety of applications be- cause it can encode correlations between words that traditional topic modeling cannot. How- ever, its expressive power ... See full document
5
Bayesian Hidden Topic Markov Models
... correlated topic modeling (CTM) allows for inference on the topic correlation struc- ture to be performed, it does not allow for the evolution of topics over ...correlated topic model, re- laxes the ... See full document
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