A Kind Introduction to Lexical and Grammatical Aspect, with a Survey of Computational Approaches (2023)

Abstract: Aspectual meaning refers to how the internal temporal structure of situations is presented. This includes whether a situation is described as a state or as an event, whether the situation is finished or ongoing, and whether it is viewed as a whole or with a focus on a particular phase. This survey gives an overview of computational approaches to modeling lexical and grammatical aspect along with intuitive explanations of the necessary linguistic concepts and terminology. In particular, we describe the concepts of stativity, telicity, habituality, perfective and imperfective, as well as influential inventories of eventuality and situation types. Aspect is a crucial component of semantics, especially for precise reporting of the temporal structure of situations, and future NLP approaches need to be able to handle and evaluate it systematically.

## introduction lexical and grammatical aspect play essential roles in semantic interpretation @xcite , and yet even state-of-the-art natural language understanding (nlu) systems do not address these linguistic phenomena systematically @xcite . consider this example: an nlu-based personal assistant, noticing the boarding time of a flight, tells a passenger (who is still shopping at the airport) "you miss flights" (i.e. on a regular basis) instead of "you are missing the flight" (now!). the traveler might misinterpret this utterance as chit-chat and indeed miss the flight. aspectual encoding errors impair fluid and correct communication. while there has been a notable amount of work on modeling lexical and grammatical aspect in the computational linguistics community @xcite @xcite , this area is still a niche in natural language processing (nlp). in this paper, we survey the state of research in this area and argue that a good computational understanding of lexical and grammatical aspect is tom is climbing. ongoing event, progressive, activity tom climbed the mountain (yesterday). ## inherent lexical aspect here we explain two fundamental notions related to inherent lexical aspect: stativity refers to whether a situation is viewed as an event or state, and telicity refers to whether an endpoint is visible in a clause. ## grammatical aspect / viewpoint phenomena treated as grammatical aspect or viewpoint @xcite take different views (the entire situation vs. a part of it), or signal recurrence. ## situation entities in this section, we review work on automatically classifying situation entity (se) types (see inventory in appendix d.3). ses are "semantic concepts organized according to their internal temporal properties" @xcite . they are introduced to the discourse by a clause's verb constellation, i.e., the clause's main verb and its arguments and modifiers. deciding on the type of an se thus involves the combination of lexical and syntactic factors. in contrast to eventuality types (section 3.3), situation entities capture the linguistic nature of the clause @xcite . events include all dynamic verb constructions referring to particular non-recurring situations, no matter whether there is a pre-defined endpoint or not. they may occur in the perfective or imperfective. generalizing sentences correspond to habituals as described in section 4.2, with the exception of generic sentences that comprise all sentences making statements about kinds @xcite . datasets. @xcite present 6065 clauses taken from the brown corpus @xcite and muc-6 @xcite , manually annotated with se types. the sitent dataset @xcite consists of 40,000 clauses from 13 genres annotated for se types with substantial agreement @xcite . @xcite crowdsource udw-g, a dataset of 37,146 arguments and 33,114 predicates in 16,222 sentences with continuous judgments on a scale from 1 to 5 with distinctions essentially following the schema of @xcite . modeling. @xcite use a maximum entropy sequence tagger with part-of-speech and ccg-based @xcite syntactic features to predict labels for each sentence separately. in an oracle experiment, they show that including the label of the previous sentence(s) as a feature improves performance. @xcite present the first true sequence labeling approach to se types classification using conditional random fields @xcite with distributional features in the form of brown cluster ids @xcite and syntactic-semantic features. @xcite propose to classify predicational aspect with distributional semantics, using non-contextualized word embeddings and sums of the word vectors of the target words and their context words. several works @xcite @xcite find that sequence information mostly helps when training in-domain and has the biggest impact on identifying generic sen-tences, which often require discourse context for classification, even by human annotators @xcite . se types have also been modeled using a variety of neural approaches. @xcite employ a gru-based @xcite rnn with an attention mechanism. @xcite dynamically build context-aware clause representations, informed by their paragraph-wide contexts. they propose a hierarchical recurrent neural network that reads entire paragraphs at once, learning interdependencies for clauses. their model uses word2vec @xcite embeddings for words, and first computes contextualized word embeddings using a bilstm over the entire paragraph. clause embeddings are formed by max pooling over the word embeddings of each clause, and then clause embeddings are further contextualized via another bilstm. for further improving se classification, they add a crf layer on top, and finally predict se labels via softmax. recently, bert @xcite and gpt-2 @xcite have been employed for the classification task by @xcite , who also compare to using parbert @xcite . while bert only considers one clause at a time, parbert reads several sentences at once, separated by [sep] tokens, and then uses the embeddings of the [sep] tokens to predict a label for the corresponding clause. comparing a variety of neural models based on non-contextualized word embeddings on their situation-entity related regression tasks, @xcite reach levels similar to human agreement. the computational problem of identifying se types has recently been studied extensively. overall, se patterns are specific to the domain, genre or discourse mode @xcite . future research could use se-style analysis for nlp tasks like temporal processing or information extraction. in this section, we discuss potential steps that could lead to more successful modeling of aspectual information, as well as how to leverage that information in nlp applications. ## conclusion this paper provides an overview of the linguistic concepts and terminology associated with aspect, and also surveys existing datasets and prior work in computational modeling of aspect. because the expression of aspect varies across languages, the proper incorporation of aspect into downstream nlp tasks requires language-dependent research rooted in the language-independent categories surveyed here. the semantic concepts expressed by lexical and grammatical aspect play essential roles in semantic interpretation, and their proper coding and evaluation must not be neglected if we are to one day develop human-level nlu systems.