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

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

Parallel texts alignment

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaAlignment of parallel texts (texts that are translation of each other) is a required step for many applications that use parallel texts, including statistical machine translation, automatic extraction of translation equivalents, automatic creation of concordances, etc. This dissertation presents a new methodology for parallel texts alignment that departs from previous work in several ways. One important departure is a shift of goals concerning the use of lexicons for obtaining correspondences between the texts. Previous methods try to infer a bilingual lexicon as part of the alignment process and use it to obtain correspondences between the texts. Some of those methods can use external lexicons to complement the inferred one, but they tend to consider them as secondary. This dissertation presents several arguments supporting the thesis that lexicon inference should not be embedded in the alignment process. The method described complies with this statement and relies exclusively on externally managed lexicons to obtain correspondences. Moreover, the algorithms presented can handle very large lexicons containing terms of arbitrary length. Besides the exclusive use of external lexicons, this dissertation presents a new method for obtaining correspondences between translation equivalents found in the texts. It uses a decision criteria based on features that have been overlooked by prior work. The proposed method is iterative and refines the alignment at each iteration. It uses the alignment obtained in one iteration as a guide to obtaining new correspondences in the next iteration, which in turn are used to compute a finer alignment. This iterative scheme allows the method to correct correspondence errors from previous iterations in face of new information

Word-to-Word Models of Translational Equivalence

Parallel texts (bitexts) have properties that distinguish them from other kinds of parallel data. First, most words translate to only one other word. Second, bitext correspondence is noisy. This article presents methods for biasing statistical translation models to reflect these properties. Analysis of the expected behavior of these biases in the presence of sparse data predicts that they will result in more accurate models. The prediction is confirmed by evaluation with respect to a gold standard -- translation models that are biased in this fashion are significantly more accurate than a baseline knowledge-poor model. This article also shows how a statistical translation model can take advantage of various kinds of pre-existing knowledge that might be available about particular language pairs. Even the simplest kinds of language-specific knowledge, such as the distinction between content words and function words, is shown to reliably boost translation model performance on some tasks. Statistical models that are informed by pre-existing knowledge about the model domain combine the best of both the rationalist and empiricist traditions

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Identifying Semantic Divergences in Parallel Text without Annotations

Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model of bilingual semantic similarity which can be trained for any parallel corpus without any manual annotation. We show that our semantic model detects divergences more accurately than models based on surface features derived from word alignments, and that these divergences matter for neural machine translation.Comment: Accepted as a full paper to NAACL 201