Automatic Derivation of Semantic Representations for T hai Serial Verb Constructions: A Grammar-Based Approach (2024)

Abstract: Deep semantic representations are useful for many NLU tasks (Droganova and Zeman 2019; Schuster and Manning-2016). Manual annotation to build these representations is time-consuming, and so automatic approaches are preferred (Droganova and Zeman 2019; Bender et al. 2015). This paper demonstrates how rich semantic representations can be automatically derived for Thai Serial Verb Constructions (SVCs), where the semantic relationship between component verbs is not immediately clear from the surface forms. I present the first fully-implemented HPSG analysis for Thai SVCs, deriving appropriate semantic representations (MRS; Copestake et al. 2005) from syntactic features, implemented within a DELPH-IN computational grammar (Slayden 2009). This analysis increases verified coverage of SVCs by 73% and decreases ambiguity by 46%. The final grammar can be found at: https://github.com/VipashaB94/ThaiGrammar

## introduction this paper presents the first fully-implemented analysis of a broad range of thai svcs in a computational grammar. an example of a thai svc is seen in (1). 1 my grammar implementation uses hpsg @xcite , and produces semantic representations in the minimal recursion semantics (mrs) framework @xcite . these representations model the semantic relationships in the construction, which are derived from the syntactic features of component verbs. the analysis was implemented on the basis of a delph-in computational grammar, originally developed by @xcite . the final grammar was tested against 216 development sentences, 205 regression sentences, and 85 held-out sentences, of which 77 were from naturally-occurring data. i show that this implementation increases verified coverage of thai svcs by 73% and decreases ambiguity by 46% on held-out data. semantic parsing is beneficial for performing various natural language understanding (nlu) tasks such as biomedical text mining or open domain relation extraction @xcite . rich semantic representations can greatly improve the performance of systems on such tasks. for example, in dependency parsing, dependency trees containing deep semantic representations are more useful than surfacesyntactic dependency trees @xcite , which often rely too strongly on the surface structure of sentences, and do not show the relationships between content words @xcite . arriving at these deep semantic representations requires complex semantic annotation @xcite , which can be either manual or grammar-driven. for example, the enhanced (and enhanced ++) universal dependency representations aim to make certain implicit relationships between content words more explicit by adding relations and augmenting relation names @xcite . alternatively, the english resource grammar @xcite @xcite takes a grammar-driven approach to produce compositional meaning annotations, and can successfully derive syntactic and semantic analyses for 85-95% of utterances in english text corpora @xcite . manual annotation, particularly for previously unannotated languages, is time-consuming and resource intensive, and so automatic approaches are extremely beneficial @xcite . @xcite argue that task-and domain-independent, automatically derivable methods to generate semantic representations would benefit the development of nlu systems, making them more comprehensive, consistent, and scalable. this can be achieved by using a compositional, linguistically-informed approach @xcite . this paper focuses on the automatic derivation of semantic representations of a specific linguistic phenomenon which requires enrichment -serial verb constructions (svc). svcs have been attested in numerous languages across west africa, central america, south-east asia, and oceania @xcite . they can have a wide range of semantic interpretations, but the specific relationships between component verbs are not explicitly indicated by the surface forms; they are instead constrained by grammatical properties. by encoding these grammatical properties, we can get from the surface string to the semantic representation. thai makes extensive use of svcs -in @xcite 's study of 76 news articles (over 10,000 words) taken from the thai-nest corpus, 74.63% of the verb tokens were part of an svc. given their frequency, to successfully complete any nlu task for thai, we must be able to deal with these constructions. the analysis developed in this paper makes the implicit relationships between component verbs explicit, without the need for manual annotation. this implementation is the first step towards building an svc library within the lingo grammar matrix customization system @xcite @xcite , which will allow for efficient implementation of the phenomenon across typologically distinct languages. ## data and categorization based on both existing literature and my own consultations with a first language speaker of thai, i identify 5 semantic categories of thai svc, arranged in 3 argument-sharing configurations, creating a total of 7 svc types (2-8). @xcite uses extraction and negation to demonstrate that these constructions are distinct from asyndetic coordination. verbs in all thai svcs share at least one argument (ibid); in most cases, this is the subject (4a) and (5-9). in some resultative constructions, the object of the first verb is the subject of the second verb, referred to as switch-function svcs (4b) @xcite . in sequential (2) and some purpose (3) svcs, both the subject and the object are shared -both semantic interpretations are available in (3). individual svcs can also interact with additional verbs or svcs to build longer, more complex structures, with more than one type of semantic relationship between component verbs. for example, (9) is a deictic-purpose svc containing a simultaneous svc. this categorization forms the basis for the type-hierarchies and feature structures in section 4. ## conclusion this paper has demonstrated how deep semantic representations of thai svcs can be automatically derived from syntactic properties of component verbs and the structure of the phrase as a whole. this was implemented into a computational grammar using an hpsg analysis, and tested against development and held-out sentences. i showed that this analysis can successfully account for thai svcs, increasing accuracy and reducing overgeneration and spurious ambiguity in both development and held-out data. this allows for the creation of richer, more precise semantic representations of thai svcs, which explicitly model the relationship between component verbs. the lingo grammar matrix @xcite @xcite both draws on and supports typological work @xcite . its goal is to combine typological research and syntactic analysis, allowing for both cross-linguistic generalizations and language-specific constraints, in order to map from surface strings to semantic representations @xcite . this analysis follows this approach, allowing for flexibility in argumentsharing, constituent structure, and verbal features used for derivation, while situated within the typological constraints presented in section 2.1.