scholarly journals Weakly Supervised SVM for Chinese- English Cross-lingual Subcategorization Lexicon Acquisition

2008 ◽  
Author(s):  
Xiwu Han ◽  
Chengguo Lv ◽  
Tiejun Zhao
Keyword(s):  
Weakly Supervised ◽  
Cross Lingual

scholarly journals Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity

2020 ◽  
pp. 1-51
Author(s):  
Ivan Vulić ◽  
Simon Baker ◽  
Edoardo Maria Ponti ◽  
Ulla Petti ◽  
Ira Leviant ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Large Scale ◽  
Representation Learning ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lexical Representations ◽  
Language Data ◽  
Weakly Supervised ◽  
Cross Lingual

We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering data sets for 12 typologically diverse languages, including major languages (e.g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e.g., Welsh, Kiswahili). Each language data set is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs, providing a representative coverage of word classes (nouns, verbs, adjectives, adverbs), frequency ranks, similarity intervals, lexical fields, and concreteness levels. Additionally, owing to the alignment of concepts across languages, we provide a suite of 66 crosslingual semantic similarity data sets. Because of its extensive size and language coverage, Multi-SimLex provides entirely novel opportunities for experimental evaluation and analysis. On its monolingual and crosslingual benchmarks, we evaluate and analyze a wide array of recent state-of-the-art monolingual and crosslingual representation models, including static and contextualized word embeddings (such as fastText, monolingual and multilingual BERT, XLM), externally informed lexical representations, as well as fully unsupervised and (weakly) supervised crosslingual word embeddings. We also present a step-by-step data set creation protocol for creating consistent, Multi-Simlex -style resources for additional languages.We make these contributions—the public release of Multi-SimLex data sets, their creation protocol, strong baseline results, and in-depth analyses which can be be helpful in guiding future developments in multilingual lexical semantics and representation learning—available via aWeb site that will encourage community effort in further expansion of Multi-Simlex to many more languages. Such a large-scale semantic resource could inspire significant further advances in NLP across languages.

Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval

2019 ◽  
Author(s):  
Lingjun Zhao ◽  
Rabih Zbib ◽  
Zhuolin Jiang ◽  
Damianos Karakos ◽  
Zhongqiang Huang
Keyword(s):  
Information Retrieval ◽  
Ad Hoc ◽  
Attentional Model ◽  
Low Resource ◽  
Weakly Supervised ◽  
Cross Lingual

scholarly journals Cross-lingual Projected Expectation Regularization for Weakly Supervised Learning

2014 ◽  
Vol 2 ◽  
pp. 55-66 ◽  
Author(s):  
Mengqiu Wang ◽  
Christopher D. Manning
Keyword(s):  
Supervised Learning ◽  
Model Uncertainty ◽  
State Of The Art ◽  
New Method ◽  
Weakly Supervised Learning ◽  
Supervised Methods ◽  
Weakly Supervised ◽  
Language Boundaries ◽  
Standard Chinese ◽  
Cross Lingual

We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages. Past approaches project labels across bitext and use them as features or gold labels for training. We propose a new method that projects model expectations rather than labels, which facilities transfer of model uncertainty across language boundaries. We encode expectations as constraints and train a discriminative CRF model using Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated on standard Chinese-English and German-English NER datasets, our method demonstrates F1 scores of 64% and 60% when no labeled data is used. Attaining the same accuracy with supervised CRFs requires 12k and 1.5k labeled sentences. Furthermore, when combined with labeled examples, our method yields significant improvements over state-of-the-art supervised methods, achieving best reported numbers to date on Chinese OntoNotes and German CoNLL-03 datasets.

scholarly journals Weakly Supervised POS Taggers Perform Poorly on Truly Low-Resource Languages

2020 ◽  
Vol 34 (05) ◽  
pp. 8066-8073
Author(s):  
Katharina Kann ◽  
Ophélie Lacroix ◽  
Anders Søgaard
Keyword(s):  
State Of The Art ◽  
Sources Of Information ◽  
High Coverage ◽  
Weak Supervision ◽  
Low Resource ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Resource Poor ◽  
Weakly Supervised ◽  
Cross Lingual

Part-of-speech (POS) taggers for low-resource languages which are exclusively based on various forms of weak supervision – e.g., cross-lingual transfer, type-level supervision, or a combination thereof – have been reported to perform almost as well as supervised ones. However, weakly supervised POS taggers are commonly only evaluated on languages that are very different from truly low-resource languages, and the taggers use sources of information, like high-coverage and almost error-free dictionaries, which are likely not available for resource-poor languages. We train and evaluate state-of-the-art weakly supervised POS taggers for a typologically diverse set of 15 truly low-resource languages. On these languages, given a realistic amount of resources, even our best model gets only less than half of the words right. Our results highlight the need for new and different approaches to POS tagging for truly low-resource languages.

Cross-Lingual Annotation Projection for Weakly-Supervised Relation Extraction

2014 ◽  
Vol 13 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Seokhwan Kim ◽  
Minwoo Jeong ◽  
Jonghoon Lee ◽  
Gary Geunbae Lee
Keyword(s):  
Relation Extraction ◽  
Weakly Supervised ◽  
Cross Lingual
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