[PDF] Top 20 Learning Word Embeddings for Low Resource Languages by PU Learning
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Learning Word Embeddings for Low Resource Languages by PU Learning
... context word that appears more often with the given word, but also avoids overwhelming the other ...of PU-learning (Hu et ...the learning pro- cess but multiplying with a smaller weight ... See full document
11
Real World Semi Supervised Learning of POS Taggers for Low Resource Languages
... in low-resource settings because type or token annotations will likely be produced by the same people, so there is a tradeoff between spending resources on one form of annotation over the ... See full document
10
On the Limitations of Unsupervised Bilingual Dictionary Induction
... The promise of zero-supervision models is that we can learn cross-lingual embeddings even for low-resource languages. On the other hand, a simi- lar distribution of embeddings requires ... See full document
11
Accurate Unsupervised Joint Named Entity Extraction from Unaligned Parallel Text
... both languages, optimizing combined contextual, word-shape and alignment ...across languages, compared to just F = ...across low- resource languages and for domain-adaptation ... See full document
9
Cross Lingual Word Embeddings for Low Resource Language Modeling
... Most languages have no established writ- ing system and minimal written ...some languages for which bilingual lexicons are available, since creating lexicons is a fun- damental task of documentary ... See full document
11
Transfer Learning across Low Resource, Related Languages for Neural Machine Translation
... a low-resource lan- guage pair using parallel data from a re- lated, also low-resource, language ...source word embeddings, to another model and continue training on the second ... See full document
6
Cross-Family Similarity Learning for Cognate Identification in Low-Resource Languages
... of word pairs annotated as cognates in the ´ Algu database is known to be far from com- plete for the vocabularies ...many word pairs in the vocabularies that are cognates, but are evaluated as ...given ... See full document
10
Learning Word Vectors for 157 Languages
... contribute word vectors trained on Wikipedia and the Common Crawl, as well as three new analogy datasets to evaluate these models, and a fast lan- guage identifier which can recognize 176 ...for languages ... See full document
5
Name Tagging for Low resource Incident Languages based on Expectation driven Learning
... Table 4 shows the performance gain of each type of expectation acquisition method. IL gazetteers cov- ered some common names, especially when the uni- versal case-based rules failed at identifying names from non-Latin ... See full document
11
Learning Bilingual Word Embeddings Using Lexical Definitions
... to learning bilingual word em- beddings often rely on word or sentence align- ment (Ruder et ...ferent languages and force the representations of corresponding sentence components to be simi- ... See full document
6
Learning bilingual word embeddings with (almost) no bilingual data
... bilingual embeddings without any bilingual ...language embeddings into the target language, a decoder that reconstructs the origi- nal embeddings, and a discriminator that distin- guishes mapped ... See full document
12
SensEmbed: Learning Sense Embeddings for Word and Relational Similarity
... a word and then used spher- ical K-means to cluster the ...the word and hence used to train embeddings for that specific meaning ...(i.e., word sense). However, these techniques either suffer ... See full document
11
WordRank: Learning Word Embeddings via Robust Ranking
... of word similarities via a low-dimensional matrix embedding, their motivation is often left ...that word embedding can be naturally viewed as a rank- ing problem due to the ranking nature of the ... See full document
11
Learning Crosslingual Word Embeddings without Bilingual Corpora
... crosslingual embeddings should preserve both monolingual and crosslingual quality which we will use as the main evaluation criterion through mono- lingual word similarity and bilingual lexicon induc- tion ... 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 ... See full document
6
A Twitter Corpus and Benchmark Resources for German Sentiment Analysis
... Deep learning for sentiment ...For languages such as English, Chinese or Arabic, there exist plenty of labeled training data for sentiment analysis, while for other European languages, the resources ... See full document
7
Supervised Phrase Table Triangulation with Neural Word Embeddings for Low Resource Languages
... supervised learning technique that improves noisy phrase translation scores obtained by phrase table ...extract word translation distributions from small amounts of source-target bilin- gual data (a ... See full document
5
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach
... bilingual lexicon induction, in which a direct dictionary between the source and target languages is not available. The semi-supervised extension of our algorithm shows that our framework can leverage unlabeled ... See full document
14
Learning Domain Sensitive and Sentiment Aware Word Embeddings
... The word “mysterious” con- veys a positive sentiment in the books domain, in- dicating how wonderful a story is, but it conveys a negative sentiment in the electronics domain typi- cally describing that a product ... See full document
11
Learning Word Meta Embeddings
... to learning metaembeddings from embeddings is the MVLSA method that learns powerful embeddings directly from multi- ple data sources (Rastogi et ...i.e., embeddings that are exclusively based ... See full document
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