![]() The experimental results proved that the participation of the proposed aligner in STS is effective. The UESTS incorporates the merits of four similarity measures: proposed alignment-based, surface-based, corpus-based, and enhanced edit distance. Third, some limitations of the state-of-the-art approaches are tackled, and different similarity methods are demonstrated to be complementary with each other by proposing an unsupervised ensemble STS (UESTS) approach. Second, three unsupervised STS approaches are proposed: string kernel-based (SK), alignment-based (AL), and weighted alignment-based (WAL). First, a new synset-oriented word aligner is presented, which relies on a huge multilingual semantic network named BabelNet. From this point, this paper has three contributions. Word alignment has been widely used in the state-of-the-art approaches. Unsupervised STS approaches are characterized by the fact that they do not require learning data, but they still suffer from some limitations. The most promising work recently presented in the literature was supervised approaches. Several approaches have been proposed in the literature to determine the semantic similarity between texts. Semantic textual similarity (STS) is the task of assessing the degree of similarity between two texts in terms of meaning. The trend generated by UMass metric shows improved topic coherence and also better cluster quality is obtained as the average entropy without eliminated values was 0.876 and with elimination was 0.906. The average elimination of incoherent aspects was found to be 28.84%. Also, frequent topical aspects across topic clusters indicate occurrence of generic aspects. The dataset comprised of product reviews from 36 product domains, containing 1000 reviews from each domain and 14 clusters per domain. BabelNet was used as the lexical resource. In this paper we have used context domain knowledge from a publicly available lexical resource to increase the coherence of topic-based aspect clusters and discriminate domain-specific semantically relevant topical aspects from generic aspects shared across the domains. ![]() However, there are two main shortcomings with these algorithms namely the cluster of topics obtained sometimes lack coherence to accurately represent relevant aspects in the cluster and the popular or common words which are referred to as the generic topics are found to occur across clusters in different domains. ![]() These topic-based aspects are then clustered to obtain semantically related groups, by algorithms such as Automated Knowledge LDA (AKL). ![]() Probabilistic topic models such as Probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA) have been popularly used to obtain thematic representations called topic-based aspects from the opinion data. Web is loaded with opinion data belonging to multiple domains. We hope that AMuSE-WSD will provide a stepping stone for the integration of meaning into real-world applications and encourage further studies in lexical semantics. In this paper, we fill this gap and propose AMuSE-WSD, the first end-to-end system to offer high-quality sense information in 40 languages through a state-of-the-art neural model for WSD. The only alternative for a user interested in applying WSD to downstream tasks is to use currently available end-to-end WSD systems, which, however, still rely on graph-based heuristics or non-neural machine learning algorithms. Unfortunately, such systems are still not available as ready-to-use end-to-end packages, making it difficult for researchers to take advantage of their performance. Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models.
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