SO at SemEval-2020 Task 7: DeepPavlov Logistic Regression with BERT Embeddings vs SVR at Funniness Evaluation

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 119–125 Barcelona, Spain (Online), December 12, 2020. 119 The UCD-Net System at SemEval-2020 Task 1: Temporal Referencing with Semantic Network Distances Paul Nulty School of Computer Science University College Dublin paul.nulty@ucd.ie David Lillis School of Computer Science University College Dublin david.lillis@ucd.ie Abstract This paper describes the UCD-Net system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We propose a novel method based on distance between temporally referenced lexical nodes in a semantic network constructed from a combination of the time specific corpora. We argue for the value of semantic networks as objects for transparent exploratory analysis and visualisation of lexical semantic change, and present an application of an interactive semantic network exploration tool for interpreting specific aspects of subtle changes in conceptual meaning. 1 Introduction SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection addresses the problem of identifying lexical semantic change using corpus pairs from different time periods in English, Swedish, German, and Latin (Schlechtweg et al., 2020). Measuring and characterising lexical semantic change is of interest to linguists as well as to researchers in the social sciences and humanities who aim to compare shifts in lexical and conceptual meaning across time periods. In common with word sense disambiguation tasks, the evaluation of systems which aim to measure lexical semantic change is complicated by the lack of obvious concrete objectives and natural sources of ground truth labels. SemEval 2020 Task 1 aims to address this by providing a human-labelled ground truth dataset across multiple languages, indicating the loss and/or gain of word senses, and the overall degree to which each word has changed meaning between the two time periods covered by the corpora. Many previous approaches to this task have been based on constructing vector space representations of target words in each of the two corpora, and measuring the difference between these representations (Tahmasebi et al., 2018). The system described in this paper differs from these approaches by its use of semantic networks to represent the meanings of words in the corpora. A semantic network in this context refers to a graph, with nodes representing words connected by edges that represent association strengths between them. Such networks have often been used in psychology and cognitive science as models for human lexical or conceptual knowledge (Steyvers and Tenenbaum, 2005). The construction of an overall semantic network to represent the structure of meaning across the corpora from both time periods provides a transparent object for exploration and visualisation of the conceptual structure that target words are located in. The system described adopts an approach inspired by temporal referencing (Dubossarsky et al., 2019), treating the time-specific earlier and later corpora as a single large corpus, constructing a single semantic network from co-occurrences derived from this overall corpus, but replacing target words (and only target words) with time-specific tokens. The change in meaning of the target words can then be measured using node distance metrics in the overall network. The …

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