[PDF] Top 20 Evaluating the Consistency of Word Embeddings from Small Data
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Evaluating the Consistency of Word Embeddings from Small Data
... words from just a single token and this process of fast mapping appears to build on concepts that are already known (Coutanche and Thompson-Schill, ...novel word vectors into a previously learned semantic ... See full document
10
A Framework for Developing and Evaluating Word Embeddings of Drug named Entity
... domain-specific word embeddings formulated with the word2vec model using PubMed and DrugBank text sources and a comprehensive intrinsic and extrinsic evaluation framework for word embeddings ... See full document
5
Towards Deep Learning in Hindi NER: An approach to tackle the Labelled Data Sparsity
... a small amount of supervised training data along with some unannotated corpus for training word embeddings yet we achieve accuracies on par with the state of the art results on the CoNLL 2003 ... See full document
7
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
... ingly high performance on the ANT dialect, while having good performance on several other dialects. This is offset, however, by the fact that the model attains a score of 0% on 6 provinces, and a very low score on 2 ... See full document
7
Urdu Word Embeddings
... One way to break out of this loop is to learn higher-level, complex representations of words and phrases that can then be used as input to bootstrap other natural language pro- cessing systems downstream. The area of ... See full document
5
Evaluating Word Embeddings Using a Representative Suite of Practical Tasks
... the word embeddings – part-of-speech tag- ging and ...dataset from the CoNLL 2000 shared task (Tjong Kim Sang and Buchholz, 2000), derived from the Wall Street ... See full document
5
A Transparent Framework for Evaluating Unintended Demographic Bias in Word Embeddings
... We evaluate three pretrained embedding mod- els: GloVe (Pennington et al., 2014), Word2vec (Mikolov et al., 2013) (trained on the large Google News corpus), and ConceptNet (Speer et al., 2017). GloVe and Word2vec ... See full document
6
Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets
... insufficient data to completely train a word embedding. The SUD data set con- sists of a few hundred people and only a fraction of these are active (Firth et ...a small data set of text ... See full document
8
Domain Adapted Word Embeddings for Improved Sentiment Classification
... DA embeddings are used to initial- ize a state-of-the-art sentence encoding algorithm, ...sentence embeddings are then classified using a logistic regression ...results from classifying sentences ... See full document
9
Empirical Study of Diachronic Word Embeddings for Scarce Data
... We proposed two initialisation schemes: the internal initialisation, more suited for low volume of data, and the backward external initialisation, more suited for higher volumes and long periods of temporal study. ... See full document
9
Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese
... each word as a sparse vector with the size of the known vocabulary having the value 1 in a given dimension and zeros else- ...where. Word embeddings, in comparison, have two evi- dent advantages: ... See full document
14
Learning bilingual word embeddings with (almost) no bilingual data
... language word nearby, mak- ing the optimization value ...corresponding embeddings nearby, making the op- timization value ...for small subsets of the vo- cabulary, we think that the structural ... See full document
12
Evaluating the Underlying Gender Bias in Contextualized Word Embeddings
... stereotype the professions as the normal embed- dings. This can be shown by the nearest neighbors of the female and male stereotyped professions, for example ‘receptionist’ and ‘librarian’ for fe- male and ‘architect’ ... See full document
7
Evaluating word embeddings with fMRI and eye tracking
... fMRI data and the eye-tracking data to vectors of aggregate statistics following the suggestions in Barrett and Søgaard (2015) and Barrett et ...gold data, as well as the two word em- ...both ... See full document
6
Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition
... pre-trained word embeddings in con- junction with Deep Learning models has be- come the de facto approach in Natural Lan- guage Processing ...off-the-shelf word embeddings tend to perform ... See full document
7
Evaluating bilingual word embeddings on the long tail
... We evaluated BWEs on the novel task of rare term mining in different domains. Our experiments show that previous approaches to bilingual lexi- con induction fail when mining rare words. We have studied techniques for ... See full document
6
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks
... between embeddings obtained from Brazilian and Eu- ropean Portuguese texts, [Fonseca and Aluisio 2016] present an extrinsic analysis on POS ...which embeddings are obtained, the ... See full document
10
Using Language Learner Data for Metaphor Detection
... Ever since conceptual metaphor theory was laid out in Lakoff and Johnson (1980), the most vex- ing question has remained a methodological one: how can conceptual metaphors be reliably identi- fied in language use? ... See full document
6
Neural based Noise Filtering from Word Embeddings
... Word embeddings aim to represent words as low-dimensional dense ...vectors, word embeddings address the problematic sparsity of word vectors and achieved impressive results in many NLP ... See full document
9
Elucidating Conceptual Properties from Word Embeddings
... dicates word embeddings may reflect some prop- erty information of a target word (Erk, 2016; Levy et ...a word would be helpful because many NLP tasks can be related to “finding words that ... See full document
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