Enhancing the Erzya-Moksha dictionary automatically with link prediction

We present a new approach to inducing a bilingual dictionary between the endangered Erzya and Moksha languages automatically based on existing dictionaries in other languages. This work is, for the most part, complementary to the Mordvin research done by PhD László Keresztes, who has demonstrated the alignment of the two language forms in morphology, lexicon and syntax (1990), and then gone on to contemplate syncretisms of the individual languages (1999). In this paper, we present an automatic data-driven method for deriving new lexicographic links between Erzya and Moksha vocabularies. We strive to describe steps in Mordvin studies involving semantic alignment of the Erzya and Moksha lexicon. We briefly remind ourselves of previous dictionary and vocabulary work with the Mordvin languages, dating back to the 17th century. The semantic alignment of this lexicon is important in measuring linguistic distance between the closely related but distinct literary languages of Erzya and Moksha. This alignment fits into a larger scheme including etymological, morphological and syntactic alignment of the two languages.Peer reviewe

Cross-Dictionary Linking at Sense Level with a Double-Layer Classifier

We present a system for linking dictionaries at the sense level, which is part of a wider programme aiming to extend current lexical resources and to create new ones by automatic means. One of the main challenges of the sense linking task is the existence of non one-to-one mappings among senses. Our system handles this issue by addressing the task as a binary classification problem using standard Machine Learning methods, where each sense pair is classified independently from the others. In addition, it implements a second, statistically-based classification layer to also model the dependence existing among sense pairs, namely, the fact that a sense in one dictionary that is already linked to a sense in the other dictionary has a lower probability of being linked to a further sense. The resulting double-layer classifier achieves global Precision and Recall scores of 0.91 and 0.80, respectively

Lemmatic machine translation

Abstract Statistical MT is limited by reliance on large parallel corpora. We propose Lemmatic MT, a new paradigm that extends MT to a far broader set of languages, but requires substantial manual encoding effort. We present PANLINGUAL TRANSLATOR, a prototype Lemmatic MT system with high translation adequacy on 59% to 99% of sentences (average 84%) on a sample of 6 language pairs that Google Translate (GT) handles. GT ranged from 34% to 93%, average 65%. PANLINGUAL TRANSLATOR also had high translation adequacy on 27% to 82% of sentences (average 62%) from a sample of 5 language pairs not handled by GT

Wiktionnaire's Wikicode GLAWIfied: a Workable French Machine-Readable Dictionary

International audienceGLAWI is a free, large-scale and versatile Machine-Readable Dictionary (MRD) that has been extracted from the French language edition of Wiktionary, called Wiktionnaire. In (Sajous and Hathout, 2015), we introduced GLAWI, gave the rationale behind the creation of this lexicographic resource and described the extraction process, focusing on the conversion and standardization of the heterogeneous data provided by this collaborative dictionary. In the current article, we describe the content of GLAWI and illustrate how it is structured. We also suggest various applications, ranging from linguistic studies, NLP applications to psycholinguistic experimentation. They all can take advantage of the diversity of the lexical knowledge available in GLAWI. Besides this diversity and extensive lexical coverage, GLAWI is also remarkable because it is the only free lexical resource of contemporary French that contains definitions. This unique material opens way to the renewal of MRD-based methods, notably the automated extraction and acquisition of semantic relations

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)

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language

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

A Generalized Constraint Approach to Bilingual Dictionary Induction for Low-Resource Language Families

The lack or absence of parallel and comparable corpora makes bilingual lexicon extraction a difficult task for low-resource languages. The pivot language and cognate recognition approaches have been proven useful for inducing bilingual lexicons for such languages. We propose constraint-based bilingual lexicon induction for closely related languages by extending constraints from the recent pivot-based induction technique and further enabling multiple symmetry assumption cycle to reach many more cognates in the transgraph. We fur- ther identify cognate synonyms to obtain many-to-many translation pairs. This article utilizes four datasets: one Austronesian low-resource language and three Indo-European high-resource languages. We use three constraint-based methods from our previous work, the Inverse Consultation method and translation pairs generated from Cartesian product of input dictionaries as baselines. We evaluate our result using the met- rics of precision, recall, and F-score. Our customizable approach allows the user to conduct cross validation to predict the optimal hyperparameters (cognate threshold and cognate synonym threshold) with various combination of heuristics and number of symmetry assumption cycles to gain the highest F-score. Our pro- posed methods have statistically significant improvement of precision and F-score compared to our previous constraint-based methods. The results show that our method demonstrates the potential to complement other bilingual dictionary creation methods like word alignment models using parallel corpora for high-resource languages while well handling low-resource languages