Termhood-based Comparability Metrics of Comparable Corpus in Special Domain

Cross-Language Information Retrieval (CLIR) and machine translation (MT) resources, such as dictionaries and parallel corpora, are scarce and hard to come by for special domains. Besides, these resources are just limited to a few languages, such as English, French, and Spanish and so on. So, obtaining comparable corpora automatically for such domains could be an answer to this problem effectively. Comparable corpora, that the subcorpora are not translations of each other, can be easily obtained from web. Therefore, building and using comparable corpora is often a more feasible option in multilingual information processing. Comparability metrics is one of key issues in the field of building and using comparable corpus. Currently, there is no widely accepted definition or metrics method of corpus comparability. In fact, Different definitions or metrics methods of comparability might be given to suit various tasks about natural language processing. A new comparability, namely, termhood-based metrics, oriented to the task of bilingual terminology extraction, is proposed in this paper. In this method, words are ranked by termhood not frequency, and then the cosine similarities, calculated based on the ranking lists of word termhood, is used as comparability. Experiments results show that termhood-based metrics performs better than traditional frequency-based metrics

In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation

Multilingual term extraction from comparable corpora : informativeness of monolingual term extraction features

Most research on bilingual automatic term extraction (ATE) from comparable corpora focuses on both components of the task separately, i.e. monolingual automatic term extraction and finding equivalent pairs cross-lingually. The latter usually relies on context vectors and is notoriously inaccurate for infrequent terms. The aim of this pilot study is to investigate whether using information gathered for the former might be beneficial for the cross-lingual linking as well, thereby illustrating the potential of a more holistic approach to ATE from comparable corpora with re-use of information across the components. To test this hypothesis, an existing dataset was expanded, which covers three languages and four domains. A supervised binary classifier is shown to achieve robust performance, with stable results across languages and domains

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

METRICC: Harnessing Comparable Corpora for Multilingual Lexicon Development

International audienceResearch on comparable corpora has grown in recent years bringing about the possibility of developing multilingual lexicons through the exploitation of comparable corpora to create corpus-driven multilingual dictionaries. To date, this issue has not been widely addressed. This paper focuses on the use of the mechanism of collocational networks proposed by Williams (1998) for exploiting comparable corpora. The paper first provides a description of the METRICC project, which is aimed at the automatically creation of comparable corpora and describes one of the crawlers developed for comparable corpora building, and then discusses the power of collocational networks for multilingual corpus-driven dictionary development

Bilingual Lexicon Extraction Using a Modified Perceptron Algorithm

전산 언어학 분야에서 병렬 말뭉치와 이중언어 어휘는 기계번역과 교차 정보 탐색 등의 분야에서 중요한 자원으로 사용되고 있다. 예를 들어, 병렬 말뭉치는 기계번역 시스템에서 번역 확률들을 추출하는데 사용된다. 이중언어 어휘는 교차 정보 탐색에서 직접적으로 단어 대 단어 번역을 가능하게 한다. 또한 기계번역 시스템에서 번역 프로세스를 도와주는 역할을 하고 있다. 그리고 학습을 위한 병렬 말뭉치와 이중언어 어휘의 용량이 크면 클수록 기계번역 시스템의 성능이 향상된다. 그러나 이러한 이중언어 어휘를 수동으로, 즉 사람의 힘으로 구축하는 것은 많은 비용과 시간과 노동을 필요로 한다. 이러한 이유들 때문에 이중언어 어휘를 추출하는 연구가 많은 연구자들에게 각광받게 되었다. 본 논문에서는 이중언어 어휘를 추출하는 새롭고 효과적인 방법론을 제안한다. 이중언어 어휘 추출에서 가장 많이 다루어지는 벡터 공간 모델을 기반으로 하고, 신경망의 한 종류인 퍼셉트론 알고리즘을 사용하여 이중언어 어휘의 가중치를 반복해서 학습한다. 그리고 반복적으로 학습된 이중언어 어휘의 가중치와 퍼셉트론을 사용하여 최종 이중언어 어휘들을 추출한다. 그 결과, 학습되지 않은 초기의 결과에 비해서 반복 학습된 결과가 평균 3.5%의 정확도 향상을 얻을 수 있었다1. 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

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 Korean–French. 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 Korean–French and Korean–Spanish. 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 2 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 French–Spanish. 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

Automatic creation of bilingual dictionaries for Finno-Ugric languages

We introduce an ongoing project whose objective is to provide linguistically based support for several small Finno-Ugric digital communities in generating online content. To achieve our goals, we collect parallel, comparable and monolingual text material for the following Finno-Ugric (FU) languages: Komi-Zyrian and Permyak, Udmurt, Meadow and Hill Mari and Northern Sami, as well as for major languages that are of interest to the FU community: English, Russian, Finnish and Hungarian. Our goal is to generate proto-dictionaries for the mentioned language pairs and deploy the enriched lexical material on the web in the framework of the collaborative dictionary project Wiktionary. In addition, we will make all of the project’s products (corpora, models, dictionaries) freely available supporting further research.</jats:p