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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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. Abstract Theories of lexical representation in production provide sophisticated accounts of the way in which information is activated during lexical access (e.g., Levelt, Roelofs, & Meyer, 1999 ), but there has been little attempt to account for the way in which the structure of the lexical entry affects the formulation processes that underlie the production of complex expressions. This paper first outlines such an account, and then reports three experiments that investigated the priming of noun-phrase structure in dialogue. Experiment 1 showed that speakers used a complex noun phrase containing a relative clause (e.g., “the square that’s red”) more often after hearing a syntactically similar noun phrase than after hearing a simple noun phrase, and that this effect was enhanced when the head noun (“square”) was repeated. Experiment 2 showed an enhanced priming effect when prime and target contained semantically related nouns (e.g., “goat” and “sheep”). Experiment 3 showed no enhanced effect when prime and target bore a close phonological relationship (e.g., “ship” and “sheep”). These results provide support for our account, and suggest that syntactic encoding may be unaffected by phonological feedback. 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 article tackles a new challenging task in computational argumentation. Given a pair of two arguments to a certain controversial topic, we aim to directly assess qualitative properties of the arguments in order to explain why one argument is more convincing than the other one. We approach this task in a fully empirical manner by annotating 26k explanations written in natural language. These explanations describe convincingness of arguments in the given argument pair, such as their strengths or flaws. We create a new crowd-sourced corpus containing 9,111 argument pairs, multi-labeled with 17 classes, which was cleaned and curated by employing several strict quality measures. We propose two tasks on this data set, namely (1) predicting the full label distribution and (2) classifying types of flaws in less convincing arguments. Our experiments with feature-rich SVM learners and Bidirectional LSTM neural networks with convolution and attention mechanism reveal that such a novel fine-grained analysis of Web argument convincingness is a very challenging task. We release the new UKPConvArg2 corpus and software under permissive licenses to the research community.