Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation

In parataxis languages like Chinese, word meanings are constructed using specific word-formations, which can help to disambiguate word senses. However, such knowledge is rarely explored in previous word sense disambiguation (WSD) methods. In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD. We first construct a large-scale Chinese lexical sample WSD dataset with word-formations. Then, we propose a model FormBERT to explicitly incorporate word-formations into sense disambiguation. To further enhance generalizability, we design a word-formation predictor module in case word-formation annotations are unavailable. Experimental results show that our method brings substantial performance improvement over strong baselines.

Now, It's Personal : The Need for Personalized Word Sense Disambiguation

Authors of text tend to predominantly use a single sense for a lemma that can differ among different authors. This might not be captured with an author-agnostic word sense disambiguation (WSD) model that was trained on multiple authors. Our work finds that WordNet's first senses, the predominant senses of our dataset's genre, and the predominant senses of an author can all be different and therefore, author-agnostic models could perform well over the entire dataset, but poorly on individual authors. In this work, we explore methods for personalizing WSD models by tailoring existing state-of-the-art models toward an individual by exploiting the author's sense distributions. We propose a novel WSD dataset and show that personalizing a WSD system with knowledge of an author's sense distributions or predominant senses can greatly increase its performance.

UoB\_UK at SemEval 2021 Task 2: Zero-Shot and Few-Shot Learning for Multi-lingual and Cross-lingual Word Sense Disambiguation.

This paper describes our submission to SemEval 2021 Task 2. We compare XLM-RoBERTa Base and Large in the few-shot and zero-shot settings and additionally test the effectiveness of using a k-nearest neighbors classifier in the few-shot setting instead of the more traditional multi-layered perceptron. Our experiments on both the multi-lingual and cross-lingual data show that XLM-RoBERTa Large, unlike the Base version, seems to be able to more effectively transfer learning in a few-shot setting and that the k-nearest neighbors classifier is indeed a more powerful classifier than a multi-layered perceptron when used in few-shot learning.

Lotus at SemEval-2021 Task 2: Combination of BERT and Paraphrasing for English Word Sense Disambiguation

In this paper, we describe our proposed methods for the multilingual word-in-Context disambiguation task in SemEval-2021. In this task, systems should determine whether a word that occurs in two different sentences is used with the same meaning or not. We proposed several methods using a pre-trained BERT model. In two of them, we paraphrased sentences and add them as input to the BERT, and in one of them, we used WordNet to add some extra lexical information. We evaluated our proposed methods on test data in SemEval- 2021 task 2.

Monolingual Word Sense Alignment as a Classification Problem

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.

Persian SemCor: A Bag of Word Sense Annotated Corpus for the Persian Language

Supervised approaches usually achieve the best performance in the Word Sense Disambiguation problem. However, the unavailability of large sense annotated corpora for many low-resource languages make these approaches inapplicable for them in practice. In this paper, we mitigate this issue for the Persian language by proposing a fully automatic approach for obtaining Persian SemCor (PerSemCor), as a Persian Bag-of-Word (BoW) sense-annotated corpus. We evaluated PerSemCor both intrinsically and extrinsically and showed that it can be effectively used as training sets for Persian supervised WSD systems. To encourage future research on Persian Word Sense Disambiguation, we release the PerSemCor in http://nlp.sbu.ac.ir.

Improving Arabic Information Retrieval Results Semantically Using Ontology

This research proposes a new way to improve the search outcome of Arabic semantics by abstractly summarizing the Arabic texts (Abstractive Summary) using natural language processing algorithms(NLP),Word Sense Disambiguation (WSD) and techniques of measuring Semantic Similarity in Arabic WordNet Ontology.