Implementation of Supervised Training Approaches for Monolingual Word Sense Alignment: ACDH-CH System Description for the MWSA Shared Task at GlobaLex 2020 (original) (raw)

A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment

2020

Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.

Monolingual Word Sense Alignment as a Classification Problem

2021

Words are defined based on their meanings in various ways in different resources. Aligning word senses across monolingual lexicographic resources increases domain coverage and enables integration and incorporation of data. In this paper, we explore the application of classification methods using manually-extracted features along with representation learning techniques in the task of word sense alignment and semantic relationship detection. We demonstrate that the performance of classification methods dramatically varies based on the type of semantic relationships due to the nature of the task but outperforms the previous experiments.

The ELEXIS system for monolingual sense linking in dictionaries

CERN European Organization for Nuclear Research - Zenodo, 2021

Sense linking is the task of inferring any potential relationships between senses stored in two dictionaries. This is a challenging task and in this paper we present our system that combines Natural Language Processing (NLP) and non-textual approaches to solve this task. We formalise linking as inferring links between pairs of senses as exact equivalents, partial equivalents (broader/narrower) or a looser relation or no relation between the two senses. This formulates the problem as a five-class classification for each pair of senses between the two dictionary entries. The work is limited to the case where the dictionaries are in the same language and thus we are only matching senses whose headword matches exactly; we call this task Monolingual Word Sense Alignment (MWSA). We have built tools for this task into an existing framework called Naisc and we describe the architecture of this system as part of the ELEXIS infrastructure, which covers all parts of the lexicographic process including dictionary drafting. Next, we look at methods of linking that rely on the text of the definitions to link, firstly looking at some basic methodologies and then implementing methods that use deep learning models such as BERT. We then look at methods that can exploit non-textual information about the senses in a meaningful way. Afterwards, we describe the challenge of inferring links holistically, taking into account that the links inferred by direct comparison of the definitions may lead to logical contradictions, e.g., multiple senses being equivalent to a single target sense. Finally, we document the creation of a test set for this MWSA task that covers 17 dictionary pairs in 15 languages and some results for our systems on this benchmark. The combination of these tools provides a highly flexible implementation that can link senses between a wide variety of input dictionaries and we demonstrate how linking can be done as part of the ELEXIS toolchain.

From Word Alignment to Word Senses, via Multilingual

Most of the successful commercial applications in language processing (text and/or speech) dispense of any explicit concern on semantics, with the usual motivations stemming from the computational high costs required, in case of large volumes of data, for dealing with semantics. With recent advances in corpus linguistics and statistical-based methods in NLP, revealing useful semantic features of linguistic data is becoming cheaper and cheaper and the accuracy of this process is steadily improving. Lately, there seems to be a growing acceptance of the idea that multilingual lexical ontologies might be the key towards aligning different views on the semantic atomic units to be used in characterizing the general meaning of various and multilingual documents. Depending on the granularity at which semantic distinctions are necessary, the accuracy of the basic semantic processing (such as word sense disambiguation) can be very high with relatively low complexity computing. The paper substantiates this statement by presenting a statistical/based system for word alignment and word sense disambiguation in parallel corpora. We describe a word alignment platform which ensures text pre-processing (tokenization, POS-tagging, lemmatization, chunking, sentence and word alignment) as required by an accurate word sense disambiguation.

MultiMirror: Neural Cross-lingual Word Alignment for Multilingual Word Sense Disambiguation

2021

Word Sense Disambiguation (WSD), i.e., the task of assigning senses to words in context, has seen a surge of interest with the advent of neural models and a considerable increase in performance up to 80% F1 in English. However, when considering other languages, the availability of training data is limited, which hampers scaling WSD to many languages. To address this issue, we put forward MULTIMIRROR, a sense projection approach for multilingual WSD based on a novel neural discriminative model for word alignment: given as input a pair of parallel sentences, our model – trained with a low number of instances – is capable of jointly aligning, at the same time, all source and target tokens with each other, surpassing its competitors across several language combinations. We demonstrate that projecting senses from English by leveraging the alignments produced by our model leads a simple mBERT-powered classifier to achieve a new state of the art on established WSD datasets in French, Germa...

UNIOR NLP at MWSA Task - GlobaLex 2020: Siamese LSTM with Attention for Word Sense Alignment

2020

In this paper we describe the system submitted to the ELEXIS Monolingual Word Sense Alignment Task. We test different systems,which are two types of LSTMs and a system based on a pretrained Bidirectional Encoder Representations from Transformers (BERT)model, to solve the task. LSTM models use fastText pre-trained word vectors features with different settings. For training the models,we did not combine external data with the dataset provided for the task. We select a sub-set of languages among the proposed ones,namely a set of Romance languages, i.e., Italian, Spanish, Portuguese, together with English and Dutch. The Siamese LSTM withattention and PoS tagging (LSTM-A) performed better than the other two systems, achieving a 5-Class Accuracy score of 0.844 in theOverall Results, ranking the first position among five teams.

Lexical Sense Alignment using Weighted Bipartite b-Matching

2019

Lexical resources are important components of natural language processing (NLP) applications providing linguistic information about the vocabulary of a language and the semantic relationships between the words. While there is an increasing number of lexical resources, particularly expert-made ones such as WordNet or FrameNet as well as collaboratively- curated ones such as Wikipedia1 or Wiktionary2 , manual construction and maintenance of such resources is a cumbersome task. This can be efficiently addressed by NLP techniques. Aligned resources have shown to improve word, knowledge and domain coverage and increase multilingualism by creating new lexical resources such as Yago , BabelNet and ConceptNet In addition, they can improve the performance of NLP tasks such as word sense disambiguation semantic role tagging and semantic relations extraction.

From Word Alignment to Word Senses, via Multilingual Wordnets

2006

Automatic Domain Assignment for Word Sense Alignment.

This paper reports on the development of a hy- brid and simple method based on a machine learning classifier (Naive Bayes), Word Sense Disambiguation and rules, for the automatic assignment of WordNet Domains to nominal entries of a lexicographic dictionary, the Senso Comune De Mauro Lexicon. The system ob- tained an F1 score of 0.58, with a Precision of 0.70. We further used the automatically as- signed domains to filter out word sense align- ments between MultiWordNet and Senso Co- mune. This has led to an improvement in the quality of the sense alignments showing the validity of the approach for domain assign- ment and the importance of domain informa- tion for achieving good sense alignments.

Implementation of Supervised Training Approaches for Monolingual Word Sense Alignment: ACDH-CH System Description for the MWSA Shared Task at GlobaLex 2020 (original) (raw)

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