[PDF] Top 20 Improving Topic Models with Latent Feature Word Representations
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Improving Topic Models with Latent Feature Word Representations
... sists of 5,512 Tweets grouped into four different top- ics (Apple, Google, Microsoft, and Twitter). Due to restrictions in Twitter’s Terms of Service, the actual Tweets need to be downloaded using 5,512 Tweet IDs. There ... See full document
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Improving Word Representations via Global Context and Multiple Word Prototypes
... Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP ...these models are built with only local context and one ... See full document
10
Latent Topic Embedding
... and word embedding are gaining significant momentum in the field of text ...General-purpose topic models such as Latent Dirichlet Allocation (LDA) (Blei et ...utilize word ... See full document
10
Improving Word Sense Disambiguation Using Topic Features
... of word sense disambiguation (WSD). This is done by using topic features constructed us- ing the latent dirichlet allocation (LDA) al- gorithm on unlabeled ... See full document
9
Topic Adaptation for Lecture Translation through Bilingual Latent Semantic Models
... bilingual topic modeling has been presented that integrates the PLSA frame- work with MDI adaptation that can effectively adapt a background language model when given a docu- ment in the source ...two topic ... See full document
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Short Text Classification Based on Latent Topic Modeling and Word Embedding
... subsequent models more objective and precise. Then we discovered the latent topics of the related universal dataset with LDA and gained topic models, by which we can inference the topics of ... See full document
7
A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings
... on improving per- formance of LDA by introducing the latent con- cept for each word, the same idea can be readily applied to other topic models that extend ... See full document
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Improving Vector Space Word Representations Using Multilingual Correlation
... automatic word alignments) should be maximally correlated (§2). We review latent se- mantic analysis (LSA), which serves as our mono- lingual VSM baseline (§3), and a suite of stan- dard evaluation tasks ... See full document
10
Learning Latent Word Representations for Domain Adaptation using Supervised Word Clustering
... generalizable latent features across domains (Blitzer et ...heuristic feature replication method to rep- resent common, source specific and target specific ...duce latent domain-invariant features as ... See full document
11
A Survey on Topics Modeling Methods over Information Filtering
... The topic model contains cluster of words with similar meanings and text,it contains different terms of topic ...of topic modeling with their advantages and disadvantages over the ...various ... See full document
8
Encoder decoder models for latent phonological representations of words
... corpus). Word embeddings were, on their own, not a strong predictor of word dura- tion (R 2 = ...phone representations by learning a vocabulary on the training data and creating sparse count vectors ... See full document
12
Compressing Neural Language Models by Sparse Word Representations
... Language models (LMs) play an important role in a variety of applications in natural language processing (NLP), including speech recognition and document ... See full document
10
Cache Augmented Latent Topic Language Models for Speech Retrieval
... language models (LMs) on the 10 hour training set using the Kaldi toolkit (Povey and others, 2011), according to the training recipe described in detail by Trmal et ...acoustic models, we report results ... See full document
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Latent feature models for large-scale link prediction
... Supervised learning methods have also been popular for link prediction [15, 16]. These methods learn predictive models on labeled training data with a set of manually designed features that capture the statistics ... See full document
11
Latent Dirichlet Allocation with Topic in Set Knowledge
... documents, and “bug” topics which can only appear in a special subset of documents. This effect was achieved by using different α hyperparameters for the 2 subsets of documents. z-labels can achieve the same effect by ... See full document
6
Observed versus latent features for knowledge base and text inference
... Once a knowledge graph is augmented with tex- tual relations, we can train the same models as be- fore, treating knowledge base and text relations in a uniform manner. However, since we are only interested in ... See full document
10
Aggregating Continuous Word Embeddings for Information Retrieval
... between topic models on dis- crete word occurrences such as PLSA/LDA and the proposed model for continuous word embed- ...generative models include a latent variable which ... See full document
10
Improving Sparse Word Representations with Distributional Inference for Semantic Composition
... neural word embeddings (Mikolov et ...richer word representations and furthermore mitigates the sparsity effect common in high-dimensional vector spaces, while remaining fully ... See full document
12
Word Alignment with Synonym Regularization
... a word alignment generative ...for word alignment. Our proposed method uses a latent topic for bilingual sentences and monolingual syn- onym pairs, which is helpful in terms of word ... See full document
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Incremental Topic Representations
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