Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, thereby loosing important information for word expectations, such as word interrelations. In this paper, we present a DSM that addresses this issue by defining verb contexts as joint syntactic dependencies. We test our representation in a verb similarity task on two datasets, showing that joint contexts achieve performances comparable to single dependencies or even better. Moreover, they are able to overcome the data sparsity problem of joint feature spaces, in spite of the limited size of our training corpus.
A number of studies in sentence processing suggests that verbs activate expectations on their typical argument nouns and vice versa @cite_20 @cite_18 and nouns do the same with other nouns occurring as co-arguments in the same events @cite_17 @cite_7 . Experimental subjects seem to exploit a rich event knowledge to activate or inhibit dynamically the representations of the potential arguments. This phenomenon, generally referred to as @cite_20 , supports the idea of a mental lexicon arranged as a web of mutual expectations.
@cite_18 @cite_7 @cite_20 @cite_17
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We explore the implications of an event-based expectancy generation approach to language understanding, suggesting that one useful strategy employed by comprehenders is to generate expectations about upcoming words. We focus on two questions: (1) What role is played by elements other than verbs in generating expectancies? (2) What connection exists between expectancy generation and event-based knowledge? Because verbs follow their arguments in many constructions (particularly in verb-final languages), deferring expectations until the verb seems inefficient. Both human data and computational modeling suggest that other sentential elements may also play a role in predictive processing and that these constraints often reflect knowledge regarding typical events. We investigated these predictions, using both short and long stimulus onset asynchrony priming. Robust priming obtained when verbs were named aloud following typical agents, patients, instruments, and locations, suggesting that event memory is organized so that nouns denoting entities and objects activate the classes of events in which they typically play a role. These computations are assumed to be an important component of expectancy generation in sentence processing. This research tests whether comprehenders use their knowledge of typical events in real time to process verbal arguments. In self-paced reading and event-related brain potential (ERP) experiments, we used materials in which the likelihood of a specific patient noun (brakes or spelling) depended on the combination of an agent and verb (mechanic checked vs. journalist checked). Reading times were shorter at the word directly following the patient for the congruent than the incongruent items. Differential N400s were found earlier, immediately at the patient. Norming studies ruled out any account of these results based on direct relations between the agent and patient. Thus, comprehenders dynamically combine information about real-world events based on intrasentential agents and verbs, and this combination then rapidly influences online sentence interpretation. An increasing number of results in sentence and discourse processing demonstrate that comprehension relies on rich pragmatic knowledge about real-world events, and that incoming words incrementally activate such knowledge. If so, then even outside of any larger context, nouns should activate knowledge of the generalized events that they denote or typically play a role in. We used short stimulus onset asynchrony priming to demonstrate that (1) event nouns prime people (sale–shopper) and objects (trip–luggage) commonly found at those events; (2) location nouns prime people animals (hospital–doctor) and objects (barn–hay) commonly found at those locations; and (3) instrument nouns prime things on which those instruments are commonly used (key–door), but not the types of people who tend to use them (hose–gardener). The priming effects are not due to normative word association. On our account, facilitation results from event knowledge relating primes and targets. This has much in common with computational models like LSA or BEAGLE in which one word primes another if they frequently occur in similar contexts. LSA predicts priming for all six experiments, whereas BEAGLE correctly predicted that priming should not occur for the instrument–people relation but should occur for the other five. We conclude that event-based relations are encoded in semantic memory and computed as part of word meaning, and have a strong influence on language comprehension.