An Argument Structure Construction Treebank (2023)

Abstract: In this paper we introduce a freely available treebank that includes argument structure construction (ASC) annotation. We then use the treebank to train probabilistic annotation models that rely on verb lemmas and/ or syntactic frames. We also use the treebank data to train a highly accurate transformer-based annotation model (F1 = 91.8%). Future directions for the development of the treebank and annotation models are discussed.

## introduction in cognitive linguistics, a construction represents a form-meaning pair. in english, for example, the verb form laughed prototypically represents a particular action in the past wherein an entity expresses joy, mirth, or scorn "with a chuckle or explosive vocal sound" @xcite . constructions exist at all levels of language (e.g., morphological, lexical, syntactic/argument structure, etc.; @xcite . therefore, while we can analyze laughed as a particular formmeaning pair, we can also consider the morphological level, wherein the form laughed represents a schematic past-tense construction denoting an event that occurred in the past (laugh verb + -ed past ). constructions also exist at the syntactic/lexicogrammatical level, wherein a verb and its argument structure constitute a form that corresponds to a propositional meaning (e.g., @xcite @xcite @xcite . these constructions are referred to as argument structure constructions (ascs). for example, they agent laughed verb represents an intransitive asc, and they agent laughed verb him theme [out of the room] goal represents a caused-motion construction. research has suggested that ascs are psycholinguisticly real and that both the schematic argument structure (e.g., agent-verb-theme-goal) and the verb that fills them (e.g., laugh) contribute to sentence meaning (e.g., @xcite . ascs and language learning: analyzing the relationship between asc use and productive language development and proficiency has been an increasingly important area of investigation in both first (l1) and second (l2) language learning research (e.g., @xcite @xcite @xcite @xcite @xcite . research suggests that individuals first learn fixed form-meaning pairs that occur frequently in their language experiences. through more [and varied] language experiences, individuals learn that some pieces of a fixed formeaning-pair is schematic (e.g., the verb slot). they then tend to overgeneralize the items that can fill a particular slot. through even more language experiences, they tune their linguistic system to the particular items that tend to occur in a particular slot in a particular construction (see, e.g., @xcite @xcite . for later development (at least in l2 contexts), research has shown that more advanced users tend to use a wider range of ascs (e.g., @xcite and verb-asc combinations (e.g., @xcite and (on average) more strongly associated verb-asc combinations @xcite . extracting ascs from corpora: an important issue in studies that analyze the characteristics of asc use is the method used to identify ascs and their verbs. many studies use a manual approach to identify ascs. while this is appropriate for smallscale studies that measure input directly and/or investigate a limited set of ascs (e.g., @xcite ) such an approach puts practical limits the amount of data that can be analyzed. given the increase in availability of large datasets of learner data (e.g., @xcite @xcite and the increased use of reference corpora as a representation of language experiences (e.g., @xcite , automatic methods of asc extraction have been proposed. these have primarily included either the use of syntactic frames as ascs (e.g., @xcite @xcite or rule-based systems that rely on syntactic frames and explicit lexical information @xcite . to date, however, no approaches have used machinelearning techniques to predict ascs directly, primarily because no asc treebank is currently available. ## extracting ascs from natural language data ascs have been extracted from corpora for a range of research purposes. these include (among others), investigating alternation (e.g., dative alternation in english; e.g, @xcite , verb-construction contingencies (e.g., @xcite @xcite , the validity of using corpus data to represent the mental construction of l1 and l2 users (e.g., @xcite , and investigating language proficiency and/or development (e.g., @xcite @xcite . ## results the results indicated that all models performed well above the simple baseline accuracy (@xmath0.307 when all ascs are tagged as tran_s). the transformer model achieved the highest overall classification accuracy (@xmath1.918), followed by the verb lemma + syntactic frame model, the syntactic frame model, and the verb lemma model. with regard to individual asc types, the transformer model also achieved the highest f1 score for each of the 9 ascs represented in the treebank (inclusive of a tie with the lemma model for the attr asc). the results for the four models (f1 scores) are summarized in ## discussion in this study, we introduce a treebank with asc annotations and an automated asc annotation model. below, we discuss features of and future directions for the corpus and the prediction models. we also discuss future directions with regard to applied research using the model. ## conclusion in this study, we introduce publicly available and open-source treebank annotated with ascs. we also present a highly accurate asc annotation model, which performs much better (@xmath2.918) than previously reported rule-based systems (@xmath3.820; @xcite . while improvements can be made with regard to the size and representativeness of the treebank, the results of this study suggest that future treebank annotation efforts would be beneficial to researchers interested in examining asc use at scale.