[PDF] Top 20 Learning Word Embeddings without Context Vectors
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Learning Word Embeddings without Context Vectors
... Datasets for word similarity evaluation consist of pairs of words rated by humans. We use the following well-known English similarity datasets: MEN-3k (Bruni et al., 2014), MTurk-287 (Ha- lawi et al., 2012), ... See full document
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Unsupervised Learning of Style sensitive Word Vectors
... unsupervised learning of style-sensitive word vectors, which extends CBOW by distinguishing nearby contexts and wider con- ...between word pairs was scored by ...leads word ... See full document
7
K Embeddings: Learning Conceptual Embeddings for Words using Context
... K- Embeddings results versus the results of a relaxed evaluation for CBOW, which considers the top K embeddings instead of the ...K embeddings, the overall (combined) performance for different K ... See full document
6
Enriching Word Sense Embeddings with Translational Context
... the word vectors using an approach called skip-gram with negative sampling (SGNS) (Mikolov et ...treats word–context pairs ac- tually occurring in a corpus as positive training examples, and ... See full document
8
Beyond Context: A New Perspective for Word Embeddings
... most word embedding models is incomplete. Besides the context, the internal information of a word also characterizes its ...the word prediction task as a multi- label classification ... See full document
11
Querying Word Embeddings for Similarity and Relatedness
... each word has led to the suggestion that distinct representations, and perhaps even distinct learn- ing models, are needed for optimal performance on these distinct tasks (Mandera et ...as word embedding ... See full document
10
Learning Bilingual Sentiment Specific Word Embeddings without Cross lingual Supervision
... Sentimental Embeddings Continuous word representations encode the syntactic context of a word but often ignore the information of sentiment ...syntactic context but opposite sentiment ... See full document
10
Word and Document Embedding with vMF Mixture Priors on Context Word Vectors
... the word vectors that are ob- tained by standard models is essentially ...ing word embeddings from a theoretical point of view (Arora et ...the word em- bedding. Similarly, we might ... See full document
10
Neural context embeddings for automatic discovery of word senses
... I.e. context width set to 20 (10 before and 10 after), and 10 negative ...target word at ran- dom, from the ukWaC ...ICE vectors, the context width for was set to 20 when using the full ... See full document
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Learning Word Vectors for Sentiment Analysis
... Using term frequency (tf) and inverse document frequency (idf) weighting to transform the values in a VSM often increases the performance of re- trieval and categorization systems. Delta idf weight- ing (Martineau and ... See full document
9
Domain Adapted Word Embeddings for Improved Sentiment Classification
... monolingual word embed- dings across data sets in different application do- mains/contexts for the purpose of a given down- stream task such as sentiment ...ing word embeddings across different ... See full document
9
Modeling Word Meaning in Context with Substitute Vectors
... In comparison to previous results, our method achieves the best reported GAP score to date, on par with Szarvas et al. (2013b). However, we note that both Szarvas et al. (2013b) and Szarvas et al. (2013a) follow a ... See full document
11
Online Learning of Interpretable Word Embeddings
... of word embeddings with the task of word intrusion detection proposed by (Murphy et ...a word set containing top-5 words in this dimension, and intruce a noisy word from the bottom half ... See full document
6
Learning Gender Neutral Word Embeddings
... Word Embeddings Word embeddings serve as a fundamental building block for a broad range of NLP applications (dos Santos and Gatti, 2014; Bahdanau et ... See full document
7
MUSE: Modularizing Unsupervised Sense Embeddings
... the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given ...tation learning or inefficient sense ... See full document
11
Learning Word Vectors for 157 Languages
... Distributed word representations, also known as word vec- tors, have been widely used in natural language processing, leading to state of the art results for many ...a word is captured by the ... See full document
5
Joint Learning of Sense and Word Embeddings
... the word vector ac- cording to the context, MSSG predicts the nearest sense first, and then updates the gradient of the sense ...of word sense dis- crimination by clustering a word contexts, ... See full document
7
Learning Syntactic Categories Using Paradigmatic Representations of Word Context
... the learning algorithm. The first subgroup represents each word type with its context vector and clusters these vectors accordingly (Sch¨utze, ...of word types (Glober- son et ... See full document
12
Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
... tors, it was found that the vector merged the basic vectors and sentence similarities vectors can pro- duce higher accuracy than only the basic vectors. By comparing the result of BoW+CWE and the ... See full document
7
Towards Incremental Learning of Word Embeddings Using Context Informativeness
... similar vectors in a ‘special’ portion of the vector space; and 2) a snowball effect related to the ‘unfreezing’ of the background space which leads background vectors to be updated by back- propagation at ... See full document
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