SBFC: An Efficient Feature Frequency-Based Approach to Tackle Cross-Lingual Word Sense Disambiguation

Abstract

Abstract. The Cross-Lingual Word Sense Disambiguation (CLWSD) problem is a challenging Natural Language Processing (NLP) task that consists of selecting the correct translation of an ambiguous word in a given context. Different approaches have been proposed to tackle this problem, but they are often complex and need tuning and parameter optimization.In this paper, we propose a new classifier, Selected Binary Feature Combination (SBFC), for the CLWSD problem. The underlying hypothesis of SBFC is that a translation is a good classification label for new instances if the features that occur frequently in the new instance also occur frequently in the training feature vectors associated with the same translation label.The advantage of SBFC over existing approaches is that it is intuitive and therefore easy to implement. The algorithm is fast, which allows processing of large text mining data sets. Moreover, no tuning is needed and experimental results show that SBFC outperforms state-of-the-art models for the CLWSD problem w.r.t. accuracy.

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“…Gradually, clustering algorithms and machine learning have also been employed in research on sentiment hotspot discovery, topic extraction, and clustering. For instance: Kumar G K et al [26] introduced a word frequency algorithm and NLTK to create a novel paragraph summarization method that simplifies complex reading comprehension; Mourisse D et al [27] used machine learning classifiers based on word frequency and text features such as phonemes to address cross-lingual word sense disambiguation problems; Wang et al [28] optimized the K-means clustering algorithm using the Isodata algorithm for mining hot topics on Weibo; Zhang [29] utilized DL-NLP technology to mine association rules for topic classification and data mining; Yi [30] proposed a text topic classification model based on BERT and VAE for feature reconstruction; Sun, Huang [31] introduced a news text classification model that integrates LSTM and attention mechanisms; Deng Lujuan [32] used the Word2vec-GRU and CNN model to extract key semantic information features for news topic classification, constructing a method through the Softmax layer; Varsha Mittal [33] introduced a deep graph-long short-term memory (DG-LSTM) model for multi-label text classification, applied to categorize themes in Indian judicial cases.…”