Index of papers in Proc. ACL 2013 that mention
  • word embedding
liu, lemao and Watanabe, Taro and Sumita, Eiichiro and Zhao, Tiejun
Abstract
In addition, word embedding is employed as the input to the neural network, which encodes each word as a feature vector.
Introduction
We also integrate word embedding into the model by representing each word as a feature vector (Collobert and Weston, 2008).
Introduction
For the local feature vector h’ in Eq (5), we employ word embedding features as described in the following subsection.
Introduction
3.3 Word Embedding features for AdNN
word embedding is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Yang, Nan and Liu, Shujie and Li, Mu and Zhou, Ming and Yu, Nenghai
Abstract
We describe in detail how we adapt and extend the CD-DNN-HMM (Dahl et al., 2012) method introduced in speech recognition to the HMM-based word alignment model, in which bilingual word embedding is discrimina-tively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences.
DNN structures for NLP
To apply DNN to NLP task, the first step is to transform a discrete word into its word embedding , a low dimensional, dense, real-valued vector (Bengio et al., 2006).
DNN structures for NLP
Word embeddings often implicitly encode syntactic or semantic knowledge of the words.
DNN structures for NLP
Assuming a finite sized vocabulary V, word embeddings form a (L x |V|)-dimension embedding matrix WV, where L is a predetermined embedding length; mapping words to embeddings is done by simply looking up their respective columns in the embedding matrix WV.
Introduction
Most works convert atomic lexical entries into a dense, low dimensional, real-valued representation, called word embedding ; Each dimension represents a latent aspect of a word, capturing its semantic and syntactic properties (Bengio et al., 2006).
Introduction
Word embedding is usually first learned from huge amount of monolingual texts, and then fine-tuned with task-specific objectives.
Introduction
As we mentioned in the last paragraph, word embedding (trained with huge monolingual texts) has the ability to map a word into a vector space, in which, similar words are near each other.
Related Work
Most methods using DNN in NLP start with a word embedding phase, which maps words into a fixed length, real valued vectors.
Related Work
(Titov et al., 2012) learns a context-free cross-lingual word embeddings to facilitate cross-lingual information retrieval.
Training
Tunable parameters in neural network alignment model include: word embeddings in lookup table LT, parameters Wl, bl for linear transformations in the hidden layers of the neural network, and distortion parameters 3d of jump distance.
word embedding is mentioned in 23 sentences in this paper.
Topics mentioned in this paper:
Hermann, Karl Moritz and Blunsom, Phil
Experiments
Instead of initialising the model with external word embeddings , we first train it on a large amount of data with the aim of overcoming the sparsity issues encountered in the previous experiment.
Experiments
In this phase only the reconstruction signal is used to learn word embeddings and transformation matrices.
Experiments
By learning word embeddings and composition matrices on more data, the model is likely to gen-eralise better.
word embedding is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: