Improving the Performance of an Example-Based Machine Translation System Using a Domain-specific Bilingual Lexicon

Conference of 29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015 ; Conference Date: 30 October 2015 Through 1 November 2015; Conference Code:119467International audienceIn this paper, we study the impact of using a domain-specific bilingual lexicon on the performance of an Example-Based Machine Translation system. We conducted experiments for the English-French language pair on in-domain texts from Europarl (European Parliament Proceedings) and out-of-domain texts from Emea (European Medicines Agency Documents), and we compared the results of the Example-Based Machine Translation system against those of the Statistical Machine Translation system Moses. The obtained results revealed that adding a domain-specific bilingual lexicon (extracted from a parallel domain-specific corpus) to the general-purpose bilingual lexicon of the Example-Based Machine Translation system improves translation quality for both in-domain as well as outof-domain texts, and the Example-Based Machine Translation system outperforms Moses when texts to translate are related to the specific domain

From Word to Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence Researc

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

Constructing a poor man’s wordnet in a resource-rich world

International audienceIn this paper we present a language-independent, fully modular and automatic approach to bootstrap a wordnet for a new language by recycling different types of already existing language resources, such as machine-readable dictionaries, parallel corpora, and Wikipedia. The approach, which we apply here to Slovene, takes into account monosemous and polysemous words, general and specialised vocabulary as well as simple and multi-word lexemes. The extracted words are then assigned one or several synset ids, based on a classifier that relies on several features including distributional similarity. Finally, we identify and remove highly dubious (literal, synset) pairs, based on simple distributional information extracted from a large corpus in an unsupervised way. Automatic, manual and task-based evaluations show that the resulting resource, the latest version of the Slovene wordnet, is already a valuable source of lexico-semantic information

Applying corpus and computational methods to loanword research : new approaches to Anglicisms in Spanish

Understanding both the linguistic and social roles of loanwords is becoming more relevant as globalization has brought loanwords into new settings, often previously viewed as monolingual. Their occurrence has the potential to impact speech communities, in that they have the capacity to alter the semantic relationships and social values ascribed to individual elements within the existing lexicon. In order to identify broad patterns, we must turn towards large and varied sources of data, specifically corpora. This dissertation aims to tackle some of the practical issues involved in the use of corpora, while addressing two conceptual issues in the field of loanword research – the social distribution and semantic nature of loanwords. In this dissertation, I propose two methods, adapted from advances in computational linguistics, which will contribute to two different stages of loanword research: processing corpora to find tokens of interest and semantically analyzing tokens of interest. These methods will be employed in two case studies. The first seeks to explore the social stratification of loanwords in Argentine Spanish. The second measures the semantic specificity of loanwords relative to their native equivalents.Spanish and Portugues

First International Workshop on Lexical Resources

International audienceLexical resources are one of the main sources of linguistic information for research and applications in Natural Language Processing and related fields. In recent years advances have been achieved in both symbolic aspects of lexical resource development (lexical formalisms, rule-based tools) and statistical techniques for the acquisition and enrichment of lexical resources, both monolingual and multilingual. The latter have allowed for faster development of large-scale morphological, syntactic and/or semantic resources, for widely-used as well as resource-scarce languages. Moreover, the notion of dynamic lexicon is used increasingly for taking into account the fact that the lexicon undergoes a permanent evolution.This workshop aims at sketching a large picture of the state of the art in the domain of lexical resource modeling and development. It is also dedicated to research on the application of lexical resources for improving corpus-based studies and language processing tools, both in NLP and in other language-related fields, such as linguistics, translation studies, and didactics

many faces, many places (Term21)

UIDB/03213/2020 UIDP/03213/2020publishersversionpublishe

many faces, many places (Term21)

UIDB/03213/2020 UIDP/03213/2020Proceedings of the LREC 2022 Workshop Language Resources and Evaluation Conferencepublishersversionpublishe