Language classification from bilingual word embedding graphs

We study the role of the second language in bilingual word embeddings in monolingual semantic evaluation tasks. We find strongly and weakly positive correlations between down-stream task performance and second language similarity to the target language. Additionally, we show how bilingual word embeddings can be employed for the task of semantic language classification and that joint semantic spaces vary in meaningful ways across second languages. Our results support the hypothesis that semantic language similarity is influenced by both structural similarity as well as geography/contact.Comment: To be published at Coling 201

Bilingual dictionary generation and enrichment via graph exploration

In recent years, we have witnessed a steady growth of linguistic information represented and exposed as linked data on the Web. Such linguistic linked data have stimulated the development and use of openly available linguistic knowledge graphs, as is the case with the Apertium RDF, a collection of interconnected bilingual dictionaries represented and accessible through Semantic Web standards. In this work, we explore techniques that exploit the graph nature of bilingual dictionaries to automatically infer new links (translations). We build upon a cycle density based method: partitioning the graph into biconnected components for a speed-up, and simplifying the pipeline through a careful structural analysis that reduces hyperparameter tuning requirements. We also analyse the shortcomings of traditional evaluation metrics used for translation inference and propose to complement them with new ones, both-word precision (BWP) and both-word recall (BWR), aimed at being more informative of algorithmic improvements. Over twenty-seven language pairs, our algorithm produces dictionaries about 70% the size of existing Apertium RDF dictionaries at a high BWP of 85% from scratch within a minute. Human evaluation shows that 78% of the additional translations generated for dictionary enrichment are correct as well. We further describe an interesting use-case: inferring synonyms within a single language, on which our initial human-based evaluation shows an average accuracy of 84%. We release our tool as free/open-source software which can not only be applied to RDF data and Apertium dictionaries, but is also easily usable for other formats and communities.This work was partially funded by the Prêt-à-LLOD project within the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 825182. This article is also based upon work from COST Action CA18209 NexusLinguarum, “European network for Web-centred linguistic data science”, supported by COST (European Cooperation in Science and Technology). It has been also partially supported by the Spanish projects TIN2016-78011-C4-3-R and PID2020-113903RB-I00 (AEI/FEDER, UE), by DGA/FEDER, and by the Agencia Estatal de Investigación of the Spanish Ministry of Economy and Competitiveness and the European Social Fund through the “Ramón y Cajal” program (RYC2019-028112-I)

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