[PDF] Top 20 Incorporating topic information into semantic analysis models
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Incorporating topic information into semantic analysis models
... to topic-classification underscores the difference between the two ...that incorporating topic information along the lines suggested in this paper will be a step towards solving some of these ... See full document
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Incorporating Semantic Word Representations into Query Expansion for Microblog Information Retrieval
... document as a relevant document, but this condition is often not satisfied, especially on microblog retriev- al tasks. Studies have shown that this method can im- prove the average accuracy of information ... See full document
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Semantic Language Models for Topic Detection and Tracking
... their semantic classes through a new semantic lan- guage modeling approach, casting the link detection task as a two-class classification problem and learning the op- timum linear discriminant function ... See full document
6
Discovering Latent Structure in Task Oriented Dialogues
... language models are likely to pro- duce similar probability distributions over ...By incorporating topic information, our proposed models ... See full document
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Employing Topic Models for Pattern based Semantic Class Discovery
... one semantic class for a given ...multiple semantic classes for one ...of topic modeling, maybe the efforts of using topic model for Word Sense Disambiguation (WSD) are most relevant to our ... See full document
9
Word Sense Disambiguation Incorporating Lexical and Structural Semantic Information
... stochastic models (Riezler et ...sense information together with symbolic grammars and statistical ...lexicalized models comes from the use of a small set of function ... See full document
9
Topic Adaptation for Lecture Translation through Bilingual Latent Semantic Models
... bilingual topic modeling for language model adaptation by combining text in the source and target language into very short documents and performing Probabilistic Latent Semantic Analysis (PLSA) ... See full document
9
TSDPMM: Incorporating Prior Topic Knowledge into Dirichlet Process Mixture Models for Text Clustering
... prior topic- s can be represented as sets of words, which are available in many real-world ...supervised information to en- hance the unsupervised DPMM for text ...for incorporating pri- or ...known ... See full document
6
Topic Modeling: A Comprehensive Review
... beings. Topic modeling is a technique comes with group of algorithms that reveal, discover and annotate thematic structure in collection of documents ...advanced information retrieval techniques [2][3] and ... See full document
16
Incorporating Side Information into Recurrent Neural Network Language Models
... description, topic head- line) of a textual ...ical analysis of various ways of injecting such in- formation into a distributed representation, which is then incorporated into either the input, hidden, or ... See full document
6
Incorporating social role theory into topic models for social media content analysis
... regularized topic model that is flexible enough to cap- ture the key elements in ...participation, information sources who post news and have a large number of followers, and information seekers or ... See full document
15
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
... from sequential context of words for learning word representations (Mikolov et al., 2013a; Pen- nington et al., 2014). More recently, this approach has been extended to include syntactic contexts (Levy and Goldberg, ... See full document
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Reddit Temporal N gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic based Latent Semantic Analysis
... A word n-gram is a continuous sequence of n words from a corpus of texts or speech. Word n-gram language models are widely used in Natural Language Processing (NLP), such as speech recognition, machine ... See full document
12
Analysis of Semantic Information via Information Reports
... is semantic information? This is after all the topic we are going to deal ...of semantic information, our choice of the very term “semantic information” apparently ... See full document
6
Are Semantically Coherent Topic Models Useful for Ad Hoc Information Retrieval?
... coherent topic models were modeled using 5 feedback documents and 20 top- ics for the WT10g collection, and this parame- ter combination also achieves the best retrieval re- ...the topic model ... See full document
5
Optimizing Semantic Coherence in Topic Models
... both topic size (top) and our coher- ence metric ...for topic size vs. 0.94 for coherence and AUC 0.79 for topic size ...regression analysis on the binary variable “is this topic bad”. ... See full document
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Incorporating Lexical Priors into Topic Models
... seed topic (or group) with a distribution over the regu- lar ...partial information rather than high-level ...used information gain to select the discriminating seed ... See full document
10
Aggregating Continuous Word Embeddings for Information Retrieval
... trieval, Sivic and Zisserman proposed to use a tf- idf weighting of the BoV vector and an inverted file for efficient matching (Sivic and Zisserman, 2003). As another example, pLSA, LDA and their many variations have ... See full document
10
An Entity Topic Model for Entity Linking
... The topic number of our model is T T = 300 = 300 (will be empirically set in Sect ...entity- topic model, we run 500 500 iterations of our Gibbs sampling algorithm to ... See full document
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Detection of Cyber bulling Based on the Automatic Code of Marginalized Denoising Improved Semantic
... short and contain a lot of informal language and misspellings, robust representations for these messages are required to reduce their ambiguity. Even worse, the lack of sufficient high-quality training data, i.e., data ... See full document
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