[PDF] Top 20 K Embeddings: Learning Conceptual Embeddings for Words using Context
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K Embeddings: Learning Conceptual Embeddings for Words using Context
... our K- Embeddings results versus the results of a relaxed evaluation for CBOW, which considers the top K embeddings instead of the ...the K embeddings, the overall (combined) ... See full document
6
Context Dependent Sense Embedding
... for learning sense ...sense embeddings dependent on word embeddings and hence avoid the problem with inaccurate embeddings of polysemous ...neighboring words which can help us ... See full document
9
Elucidating Conceptual Properties from Word Embeddings
... word embeddings may reflect some prop- erty information of a target word (Erk, 2016; Levy et ...2015). Learning the properties of a word would be helpful because many NLP tasks can be related to “finding ... See full document
5
Dependency Based Word Embeddings
... ing words as discrete and distinct symbols is in- sufficient for many tasks, and suffers from poor ...the words “pizza” and “hamburger” are completely unrelated: even if we know that the word “pizza” is a ... See full document
7
Parsing with Context Embeddings
... predict words in a sentence using both the left and the right con- ...word embeddings and context embeddings from the language ...Word embeddings represent the general features ... See full document
8
Learning Composition Models for Phrase Embeddings
... ponent words in a ...of words according to their syntactic properties ...phrase. Using one tensor (not word-specific) to compose two embed- ding vectors (has not been tested on phrase similar- ity ... See full document
16
Spell Once, Summon Anywhere: A Two-Level Open-Vocabulary Language Model
... known words can help us deal with unknown words in open-vocabulary NLP ...novel words in context, we obtain an open-vocabulary language ...known words, embeddings are naturally ... See full document
8
Words are Vectors, Dependencies are Matrices: Learning Word Embeddings from Dependency Graphs
... The dependency-matrices modify meanings captured in the context-word vectors. In this behaviour, they are similar to representations of relational words, such as verbs or adjectives, in Compositional Se- ... See full document
12
Embedding Imputation with Grounded Language Information
... of embeddings as input representations for a wide range of natu- ral language tasks, imputation of embeddings for rare and unseen words is a critical problem in language ...involves learning ... See full document
6
Attention based Semantic Priming for Slot filling
... word context to enhance the discriminating power of se- quence labelling ...deep learning architecture to simulate the semantic priming ...word embeddings to characterise not only the context ... See full document
5
Adaptive Joint Learning of Compositional and Non Compositional Phrase Embeddings
... Learning embeddings of words and phrases has been widely studied, and the phrase embeddings have proven effective in many language process- ing tasks, such as machine translation (Cho et ... See full document
11
Empirical Evaluation of Active Learning Techniques for Neural MT
... an initial NMT model. We then randomly sam- ple 50% from the remaining data to the unlabeled dataset U (∼ 1M) used for simulating the AL ex- periments. Note that we do the random sampling just once and fix L and U for ... See full document
10
The Role of Context Types and Dimensionality in Learning Word Embeddings
... of words, parts-of-speech and de- pendency ...word embeddings, which were pre-trained on unlabeled data, yields improved ...of embeddings to initialize the NNDEP parser and compared their per- ... See full document
11
Leveraging Social Network Data to Alleviate Cold-Start Problem in Recommender Systems
... Word embeddings. Standard topic models assume individual words are exchangeable, which is essentially the same as the bag-of- words model ...or embeddings learned to use neural language models ... See full document
8
Context encoders as a simple but powerful extension of word2vec
... the words would otherwise get lost in preprocess- ing ...create embeddings that distinguish even better between the words’ different senses by taking into account, for example, if the word is used as ... See full document
5
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
... different context types and representations. Context representation plays a more importan- t role than context type for learning word embed- ...DEPS context capture functional similar- ... See full document
11
Learning Better Embeddings for Rare Words Using Distributional Representations
... to using distributional information for initialization is to use syntactic and semantic information for ...the embeddings of rare ...the context space dur- ing ...word embeddings with the ... See full document
6
context2vec: Learning Generic Context Embedding with Bidirectional LSTM
... the context word ...the context is not just a single word but an entire sentential context of a target ...target- context embedding obtained by our algorithm as a factorization of the PMI ... See full document
11
Learning Word Embeddings without Context Vectors
... The amount of negative eigenvalues of M mea- sures the deviation from the positive definiteness in some sense. To estimate it, we construct shifted PPMI matrices for Wikipedia corpora in three dif- ferent languages ... See full document
6
Towards Incremental Learning of Word Embeddings Using Context Informativeness
... frequency words before summing, outputting a rank of 192 (filtered-out words include also con- tent words like international or ...the context words set even further by removing all ... See full document
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