Automatic Recognition of Habituals: a Three-Way Classification of Clausal
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Automatic recognition of habituals: a three-way classification of clausal aspect Annemarie Friedrich Manfred Pinkal Department of Computational Linguistics Saarland University, Saarbrucken,¨ Germany afried,pinkal @coli.uni-saarland.de { } Abstract (2) (a) Bill usually drinks coffee after lunch. (habitual) This paper provides the first fully au- (b) Italians drink coffee after lunch. tomatic approach for classifying clauses (habitual) with respect to their aspectual properties as habitual, episodic or static. We bring The entailment properties of episodic and ha- together two strands of previous work, bitual (or characterizing) sentences differ substan- which address only the related tasks of tially. Also, they have different functions in dis- the episodic-habitual and stative-dynamic course. The episodic event expressed by (1a) is distinctions, respectively. Our method typically embedded in the temporal structure of a combines different sources of information narration. The habitual sentence (2a) can be used, found to be useful for these tasks. We are e.g., as an explanation (why Bill is still sitting at the first to exhaustively classify all clauses the table), or in a contrastive context (today, he or- of a text, achieving up to 80% accuracy dered a grappa instead). Generic sentences with (baseline 58%) for the three-way classifi- kind-referring subjects (2b) can also be habitual, cation task, and up to 85% accuracy for generalizing at the same time over typical mem- related subtasks (baselines 50% and 60%), bers of this kind and over situations in which they outperforming previous work. In addi- typically carry out some action. tion, we provide a new large corpus of Habitual sentences do not move narrative time, Wikipedia texts labeled according to our similar to stative clauses such as (1b). Carlson linguistically motivated guidelines. (2005) considers habituals to be aspectually sta- 1 Introduction tive. Since there are clear differences between or- dinary statives such as (1b) and habituals, we ap- In order to understand the function of a clause ply a three-way distinction for clausal aspect in within a discourse, we need to know the clause’s this work. We classify clauses as one of the three aspectual properties. The distinction between dy- categories habitual, episodic and static.1 namic and stative lexical aspect, as in exam- Through its impact on entailment properties and ples (1a) and (1b) respectively, is fundamental temporal discourse structure, the determination of (Vendler, 1957). Its automatic prediction has clausal aspect is relevant to various natural lan- been addressed previously (Siegel and McKeown, guage processing applications requiring text un- 2000; Zarcone and Lenci, 2008; Friedrich and derstanding, such as novelty detection (Soboroff Palmer, 2014). and Harman, 2005), knowledge extraction from (1) (a) Bill drank a coffee after lunch. (dynamic) text (Van Durme, 2010) or question answering (b) Bill likes coffee. (stative) (Llorens et al., 2015). Using aspectual informa- tion has been shown to improve temporal relation In this work, we focus on habituality as another identification (Costa and Branco, 2012). fundamental aspectual property. While example Some languages (e.g., Czech or Swahili) have (1a) is an episodic sentence, i.e., a sentence ex- systematic morphological markers of habituality pressing information about a particular event, the 1 same dynamic verb can be used to characterize the For clarity, we use the label static for the clausal aspect of non-episodic and non-habitual sentences. We reserve sta- default behavior of an individual or of a kind in a tive, which is more common in the literature, for the lexical certain type of situation (2). aspectual class. 2471 Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2471–2481, Lisbon, Portugal, 17-21 September 2015. c 2015 Association for Computational Linguistics. (Dahl, 1985). In other languages, there are cues context-based features) to have different impact on for habituality, such as the simple present in En- the two subtasks, and to be complementary for our glish, and the use of certain adverbials (Dahl, full three-way task. 1995). The automatic recognition of habitual sen- We reach accuracies of nearly 85% for the two tences for the latter languages is non-trivial. The subtasks of identifying static clauses and distin- work in this paper targets the English language; guishing episodic and habitual clauses (majority we leave recognition of habituality in other lan- class baselines are 60% and 50% respectively). A guages to future work. joint model for the three-way classification task The only previous work on categorizing sen- reaches an accuracy of 80% (baseline 60%). In ad- tences as episodic or habitual we know of is by dition, we show that the verb-type based linguis- Mathew and Katz (2009). They do not attempt to tic indicator features generalize well across verb categorize arbitrary sentences in ‘free text’, how- types on our tasks: for verbs unseen in the training ever, but work with a corpus of selected sentences data, accuracies drop only by 2-5%. and use gold standard parse information for their 2 Theoretical background and experiments. In particular, they consider clauses annotation guidelines containing lexically dynamic verbs only. In this work, we address the task of an exhaus- In this section, we give an overview of the seman- tive classification of all clauses of a text into the tic theory related to habituals, at the same time three aspectual classes habitual, episodic, and introducing our annotation guidelines for marking static. Static sentences include lexically stative clauses as habitual, episodic or static. clauses as well as negated or modalized clauses 2.1 Habituality containing a dynamic main verb. A computa- tional model for identifying episodic and habitual Habitual sentences express regularities in terms of clauses clearly needs to address this third class generalizations over events and activities. In se- as well if it is to be applied in a realistic set- mantic theory, habituals are formally represented ting. Linguistically, the determination of clausal using a quantifier-like operator GEN (Krifka et al., aspect depends on the recognition of the verb’s 1995): lexical aspectual class (stative or dynamic), and (3) GEN[s](s is an after-dinner situation & Bill on the recognition of any aspectual markers or is involved in s; Bill drinks a coffee in s) transformations, such as use of the perfect tense, negations or modals. Our work builds on re- In the semi-formal representation (3) of sen- sults for the related subtasks (Mathew and Katz, tence (2a) above, GEN binds a variable s ranging 2009; Siegel and McKeown, 2000; Friedrich and over situations, the first argument restricts the sit- Palmer, 2014), using both context-based and verb- uation type, and the second argument provides the type based information. activity that is typically carried out by the protago- nist in the respective situations. The GEN operator Our major contributions are: (i) We create a cor- is similar to the universal quantifier of predicate pus of 102 Wikipedia texts whose about 10,000 logic. However, habitual sentences tolerate excep- clauses are annotated as episodic, static or habitual tions: (2a) is true even if Bill does not drink a cof- with substantial agreement. This corpus allows for fee after every lunch. Also note that habituals are studying the range of linguistic phenomena related not restricted to what one would consider a matter to the clause types as defined above (compared to of habit; they can also have inanimate subjects, as previous work which uses only a small set of verbs illustrated by (4). and sentences), and provides a basis for future re- search. (ii) We provide the first fully automatic ap- (4) Glass breaks easily. (habitual) proach for this classification task, combining two classification tasks (lexical aspectual class and ha- 2.2 Clausal and lexical aspectual class bituality) that have been treated separately in pre- Clausal aspect is dependent on the lexical aspec- vious work. For an exhaustive classification of tual class (stative or dynamic), but the two lev- clauses of free text, these two classification tasks els are essentially different. Dynamic verbs ex- need to be addressed jointly. We show two dif- press events or activities (e.g., kill, fix, walk, for- ferent feature sets (verb-type based features and get), while stative verbs express states (e.g., be, 2472 like, know, own). The fundamental aspectual class Sentence (9a) can be considered either static (Siegel and McKeown, 2000) of a verb in context because of the negation (It is not the case that describes whether it is used in a stative or dynamic John smokes), or as habitual because it charac- sense before any aspectual markers or transforma- terizes John’s behavior (In any relevant situation, tions (such as use of the past/present perfect or John does not smoke). Both decisions are possible modals) have been applied. It is a function of the (Garrett, 1998), we decide for the latter possibility. main verb and a select group of complements (it This decision is supported by the observation that may differ per verb which ones are important). For (9a) is similar in its entailment properties to (10), example, the fundamental lexical aspectual class which due to the frequency adverbial never clearly of the verb make with the subject Mary and the generalizes over relevant situations (though note object cake in (5) is dynamic. English clauses in that this is not a linguistic test). past or present perfect such as (5) are static, as they focus on the post-state of an event rather than the (10) John never smokes. (habitual) event itself (Katz, 2003). Likewise, we mark modalized sentences as ha- bitual if they have a strong implicature that an (5) Mary has made a cake. ( ) static event has actually happened regularly (Hacquard, 2009), as in (11).