427 research outputs found
Corpus-driven bilingual lexicon extraction
This paper introduces some key aspects of machine translation in order to situate the role of the bilingual lexicon in transfer-based systems. It then discusses the data-driven approach to extracting bilingual knowledge automatically from bilingual texts, tracing the processes of alignment at different levels of granularity. The paper concludes with some suggestions for future work.peer-reviewe
Ridge Regression, Hubness, and Zero-Shot Learning
This paper discusses the effect of hubness in zero-shot learning, when ridge
regression is used to find a mapping between the example space to the label
space. Contrary to the existing approach, which attempts to find a mapping from
the example space to the label space, we show that mapping labels into the
example space is desirable to suppress the emergence of hubs in the subsequent
nearest neighbor search step. Assuming a simple data model, we prove that the
proposed approach indeed reduces hubness. This was verified empirically on the
tasks of bilingual lexicon extraction and image labeling: hubness was reduced
with both of these tasks and the accuracy was improved accordingly.Comment: To be presented at ECML/PKDD 201
Bilingual Lexicon Extraction from Comparable Corpora as Metasearch
International audienceIn this article we present a novel way of looking at the problem of automatic acquisition of pairs of translationally equivalent words from comparable corpora. We ๏ฌrst present the standard and extended approaches traditionally dedicated to this task. We then reinterpret the extended method, and motivate a novel model to reformulate this approach inspired by the metasearch engines in information retrieval. The empirical results show that performances of our model are always better than the baseline obtained with the extended approach and also competitive with the standard approach
Identification of Fertile Translations in Medical Comparable Corpora: a Morpho-Compositional Approach
This paper defines a method for lexicon in the biomedical domain from
comparable corpora. The method is based on compositional translation and
exploits morpheme-level translation equivalences. It can generate translations
for a large variety of morphologically constructed words and can also generate
'fertile' translations. We show that fertile translations increase the overall
quality of the extracted lexicon for English to French translation
Multilingual Lexicon Extraction under Resource-Poor Language Pairs
In general, bilingual and multilingual lexicons are important resources in many natural language processing fields such as information retrieval and machine translation. Such lexicons are usually extracted from bilingual (e.g., parallel or comparable) corpora with external seed dictionaries. However, few such corpora and bilingual seed dictionaries are publicly available for many language pairs such as KoreanโFrench. It is important that such resources for these language pairs be publicly available or easily accessible when a monolingual resource is considered.
This thesis presents efficient approaches for extracting bilingual single-/multi-word lexicons for resource-poor language pairs such as KoreanโFrench and KoreanโSpanish. The goal of this thesis is to present several efficient methods of extracting translated single-/multi-words from bilingual corpora based on a statistical method.
Three approaches for single words and one approach for multi-words are proposed. The first approach is the pivot context-based approach (PCA). The PCA uses a pivot language to connect source and target languages. It builds context vectors from two parallel corpora sharing one pivot language and calculates their similarity scores to choose the best translation equivalents. The approach can reduce the effort required when using a seed dictionary for translation by using parallel corpora rather than comparable corpora. The second approach is the extended pivot context-based approach (EPCA). This approach gathers similar context vectors for each source word to augment its context. The approach assumes that similar vectors can enrich contexts. For example, young and youth can augment the context of baby. In the investigation described here, such similar vectors were collected by similarity measures such as cosine similarity. The third approach for single words uses a competitive neural network algorithm (i.e., self-organizing mapsSOM). The SOM-based approach (SA) uses synonym vectors rather than context vectors to train two different SOMs (i.e., source and target SOMs) in different ways. A source SOM is trained in an unsupervised way, while a target SOM is trained in a supervised way.
The fourth approach is the constituent-based approach (CTA), which deals with multi-word expressions (MWEs). This approach reinforces the PCA for multi-words (PCAM). It extracts bilingual MWEs taking all constituents of the source MWEs into consideration. The PCAM
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identifies MWE candidates by pointwise mutual information first and then adds them to input data as single units in order to use the PCA directly.
The experimental results show that the proposed approaches generally perform well for resource-poor language pairs, particularly Korean and FrenchโSpanish. The PCA and SA have demonstrated good performance for such language pairs. The EPCA would not have shown a stronger performance than expected. The CTA performs well even when word contexts are insufficient. Overall, the experimental results show that the CTA significantly outperforms the PCAM.
In the future, homonyms (i.e., homographs such as lead or tear) should be considered. In particular, the domains of bilingual corpora should be identified. In addition, more parts of speech such as verbs, adjectives, or adverbs could be tested. In this thesis, only nouns are discussed for simplicity. Finally, thorough error analysis should also be conducted.Abstract
List of Abbreviations
List of Tables
List of Figures
Acknowledgement
Chapter 1 Introduction
1.1 Multilingual Lexicon Extraction
1.2 Motivations and Goals
1.3 Organization
Chapter 2 Background and Literature Review
2.1 Extraction of Bilingual Translations of Single-words
2.1.1 Context-based approach
2.1.2 Extended approach
2.1.3 Pivot-based approach
2.2 Extractiong of Bilingual Translations of Multi-Word Expressions
2.2.1 MWE identification
2.2.2 MWE alignment
2.3 Self-Organizing Maps
2.4 Evaluation Measures
Chapter 3 Pivot Context-Based Approach
3.1 Concept of Pivot-Based Approach
3.2 Experiments
3.2.1 Resources
3.2.2 Results
3.3 Summary
Chapter 4 Extended Pivot Context-Based Approach
4.1 Concept of Extended Pivot Context-Based Approach
4.2 Experiments
4.2.1 Resources
4.2.2 Results
4.3 Summary
Chapter 5 SOM-Based Approach
5.1 Concept of SOM-Based Approach
5.2 Experiments
5.2.1 Resources
5.2.2 Results
5.3 Summary
Chapter 6 Constituent-Based Approach
6.1 Concept of Constituent-Based Approach
6.2 Experiments
6.2.1 Resources
6.2.2 Results
6.3 Summary
Chapter 7 Conclusions and Future Work
7.1 Conclusions
7.2 Future Work
Reference
Bilingual Lexicon Extraction Using a Modified Perceptron Algorithm
์ ์ฐ ์ธ์ดํ ๋ถ์ผ์์ ๋ณ๋ ฌ ๋ง๋ญ์น์ ์ด์ค์ธ์ด ์ดํ๋ ๊ธฐ๊ณ๋ฒ์ญ๊ณผ ๊ต์ฐจ ์ ๋ณด ํ์ ๋ฑ์ ๋ถ์ผ์์ ์ค์ํ ์์์ผ๋ก ์ฌ์ฉ๋๊ณ ์๋ค. ์๋ฅผ ๋ค์ด, ๋ณ๋ ฌ ๋ง๋ญ์น๋ ๊ธฐ๊ณ๋ฒ์ญ ์์คํ
์์ ๋ฒ์ญ ํ๋ฅ ๋ค์ ์ถ์ถํ๋๋ฐ ์ฌ์ฉ๋๋ค. ์ด์ค์ธ์ด ์ดํ๋ ๊ต์ฐจ ์ ๋ณด ํ์์์ ์ง์ ์ ์ผ๋ก ๋จ์ด ๋ ๋จ์ด ๋ฒ์ญ์ ๊ฐ๋ฅํ๊ฒ ํ๋ค. ๋ํ ๊ธฐ๊ณ๋ฒ์ญ ์์คํ
์์ ๋ฒ์ญ ํ๋ก์ธ์ค๋ฅผ ๋์์ฃผ๋ ์ญํ ์ ํ๊ณ ์๋ค. ๊ทธ๋ฆฌ๊ณ ํ์ต์ ์ํ ๋ณ๋ ฌ ๋ง๋ญ์น์ ์ด์ค์ธ์ด ์ดํ์ ์ฉ๋์ด ํฌ๋ฉด ํด์๋ก ๊ธฐ๊ณ๋ฒ์ญ ์์คํ
์ ์ฑ๋ฅ์ด ํฅ์๋๋ค. ๊ทธ๋ฌ๋ ์ด๋ฌํ ์ด์ค์ธ์ด ์ดํ๋ฅผ ์๋์ผ๋ก, ์ฆ ์ฌ๋์ ํ์ผ๋ก ๊ตฌ์ถํ๋ ๊ฒ์ ๋ง์ ๋น์ฉ๊ณผ ์๊ฐ๊ณผ ๋
ธ๋์ ํ์๋ก ํ๋ค. ์ด๋ฌํ ์ด์ ๋ค ๋๋ฌธ์ ์ด์ค์ธ์ด ์ดํ๋ฅผ ์ถ์ถํ๋ ์ฐ๊ตฌ๊ฐ ๋ง์ ์ฐ๊ตฌ์๋ค์๊ฒ ๊ฐ๊ด๋ฐ๊ฒ ๋์๋ค.
๋ณธ ๋
ผ๋ฌธ์์๋ ์ด์ค์ธ์ด ์ดํ๋ฅผ ์ถ์ถํ๋ ์๋กญ๊ณ ํจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ๋ก ์ ์ ์ํ๋ค. ์ด์ค์ธ์ด ์ดํ ์ถ์ถ์์ ๊ฐ์ฅ ๋ง์ด ๋ค๋ฃจ์ด์ง๋ ๋ฒกํฐ ๊ณต๊ฐ ๋ชจ๋ธ์ ๊ธฐ๋ฐ์ผ๋ก ํ๊ณ , ์ ๊ฒฝ๋ง์ ํ ์ข
๋ฅ์ธ ํผ์
ํธ๋ก ์๊ณ ๋ฆฌ์ฆ์ ์ฌ์ฉํ์ฌ ์ด์ค์ธ์ด ์ดํ์ ๊ฐ์ค์น๋ฅผ ๋ฐ๋ณตํด์ ํ์ตํ๋ค. ๊ทธ๋ฆฌ๊ณ ๋ฐ๋ณต์ ์ผ๋ก ํ์ต๋ ์ด์ค์ธ์ด ์ดํ์ ๊ฐ์ค์น์ ํผ์
ํธ๋ก ์ ์ฌ์ฉํ์ฌ ์ต์ข
์ด์ค์ธ์ด ์ดํ๋ค์ ์ถ์ถํ๋ค.
๊ทธ ๊ฒฐ๊ณผ, ํ์ต๋์ง ์์ ์ด๊ธฐ์ ๊ฒฐ๊ณผ์ ๋นํด์ ๋ฐ๋ณต ํ์ต๋ ๊ฒฐ๊ณผ๊ฐ ํ๊ท 3.5%์ ์ ํ๋ ํฅ์์ ์ป์ ์ ์์๋ค1. Introduction
2. Literature Review
2.1 Linguistic resources: The text corpora
2.2 A vector space model
2.3 Neural networks: The single layer Perceptron
2.4 Evaluation metrics
3. System Architecture of Bilingual Lexicon Extraction System
3.1 Required linguistic resources
3.2 System architecture
4. Building a Seed Dictionary
4.1 Methodology: Context Based Approach (CBA)
4.2 Experiments and results
4.2.1 Experimental setups
4.2.2 Experimental results
4.3 Discussions
5. Extracting Bilingual Lexicons
4.1 Methodology: Iterative Approach (IA)
4.2 Experiments and results
4.2.1 Experimental setups
4.2.2 Experimental results
4.3 Discussions
6. Conclusions and Future Work
Automatic Bilingual Lexicon Extraction for a Minority Target Language
PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200
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