[PDF] Top 20 Learning Word Representations with Regularization from Prior Knowledge
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Learning Word Representations with Regularization from Prior Knowledge
... LDA word list or lexicons, it is very sensitive to the quality of the corresponding ...observed from our experiment that unannotated knowledge, ...semantic knowledge, outperforms other types ... See full document
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Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
... trary prior knowledge sources into NMT using posterior regularization (Ganchev et ...represent prior knowledge sources as arbitrary real-valued fea- tures, we define the posterior ... See full document
10
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces
... work, word embeddings are a useful unsupervised tech- nique to attain initial model values or features prior to supervised ...each word in a vocabulary, the total number of parameters in the model ... See full document
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A Simple Regularization based Algorithm for Learning Cross Domain Word Embeddings
... earlier word embedding meth- ods employed the computationally expensive neu- ral network architectures (Collobert and Weston, 2008; Mikolov et ...for learning word representations, namely the ... See full document
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Word and Phrase Learning based on Prior Semantics
... the word association task, we first com- pile a set of narratives by asking nine adults to describe objects and activities in free un- constrained ...the knowledge of word segementation is ...to ... See full document
6
Co learning of Word Representations and Morpheme Representations
... able knowledge to bridge the gap between rare or unknown words and well-known words in learning word ...morphological knowledge to obtaining high-quality word ...1-of-v ... See full document
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Learning Bilingual Word Representations by Marginalizing Alignments
... uments from one language to another. The word representations our model learns as part of the alignment process are semantically plausible and ...outperform prior work, achieve results on par ... See full document
6
Knowledge Enhanced Contextual Word Representations
... In practice, these are often implemented us- ing precomputed dictionaries (e.g., CrossWikis; Spitkovsky and Chang, 2012), KB specific rules (e.g., a WordNet lemmatizer), or other heuristics (e.g., string match; Mihaylov ... See full document
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Learning Latent Word Representations for Domain Adaptation using Supervised Word Clustering
... Representation learning methods bridge do- main divergence either by differentiating domain- invariant features from domain-specific features (Daum´e III, 2007; Daum´e III et ...Bayesian prior. ... See full document
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Sound Word2Vec: Learning Word Representations Grounded in Sounds
... To be able to interact better with humans, it is crucial for machines to understand sound – a primary modality of human per- ception. Previous works have used sound to learn embeddings for improved generic semantic ... See full document
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Employing Word Representations and Regularization for Domain Adaptation of Relation Extraction
... general representations provided by word representations above, how can we learn a relation extractor from the labeled source domain data that generalizes well to new domains? In tra- ditional ... See full document
7
Word Ordering as Unsupervised Learning Towards Syntactically Plausible Word Representations
... syntactic word embeddings with- out using any human ...the word odering task is suitable for obtaining syntactic knowledge about ...syntactic word representations through ... See full document
10
Incorporating Relational Knowledge into Word Representations using Subspace Regularization
... lexical knowledge from se- mantic resources ...the word pairs as generic related words, or employ rather restrictive assumptions to model the rela- tional ...relational knowledge based on ... See full document
6
Learning Distributed Representations of Texts and Entities from Knowledge Base
... proposed representations trained with sentences. We first inspected the word representations of our model and our pre-trained representations ...one word she whose cosine similarity to ... See full document
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Ngram2vec: Learning Improved Word Representations from Ngram Co occurrence Statistics
... In this section, we only report the results of mod- els of ‘uni uni’ and ‘uni bi’ types. Using high- er order co-occurrence statistics brings immense costs (especially at the window size of 5). Levy and Goldberg (2014b) ... See full document
10
Learning Word Representations from Relational Graphs
... as knowledge representation, similarity measure- ment, and analogy ...for word representation learning, but also the semantic relations in which two words ...the word representations ... See full document
7
Aspect Extraction with Automated Prior Knowledge Learning
... called knowledge-based topic models, have been ...of prior knowledge from the user: must-links and ...allow prior knowledge to be specified by the user to guide the modeling ... See full document
12
Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
... closed if bigrams of characters are used in char- based models. In the phrase-based machine trans- lation, Xu et al. (2004) reported that CWS only showed non-significant improvements over mod- els without word ... See full document
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Prior knowledge and statistical models of learning
... The second experiment involved training participants on a particular pattern of responsesin the first phase, and testing whether they could apply that function with different parameter v[r] ... See full document
281
Second order contexts from lexical substitutes for few shot learning of word representations
... To conclude, our paper teases apart the effect of second-order context by proposing a simple second-order substitute-based method that can post-process and improve over an existing embed- ding space. Our substitute-based ... See full document
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