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Despite its substantial coverage, Nom-Bank does not account for all within-sentence arguments and ignores extra-sentential arguments altogether. These arguments , which we call implicit, are important to semantic processing, and their recovery could potentially benefit many NLP applications. We present a study of implicit arguments for a select group of(More)
Nominal predicates often carry implicit arguments. Recent work on semantic role labeling has focused on identifying arguments within the local context of a predicate; implicit arguments, however, have not been systematically examined. To address this limitation, we have manually annotated a corpus of implicit arguments for ten predicates from NomBank.(More)
Motivated by psycholinguistic findings, we are currently investigating the role of eye gaze in spoken language understanding for multimodal conversational systems. Our assumption is that, during human machine conversation, a user's eye gaze on the graphical display indicates salient entities on which the user's attention is focused. The specific domain(More)
Multimodal conversational interfaces provide a natural means for users to communicate with computer systems through multiple modalities such as speech and gesture. To build effective multimodal interfaces, automated interpretation of user multimodal inputs is important. Inspired by the previous investigation on cognitive status in multimodal human machine(More)
Collaborative filtering identifies information interest of a particular user based on the information provided by other similar users. The memory-based approaches for collaborative filtering (e.g., Pearson correlation coefficient approach) identify the similarity between two users by comparing their ratings on a set of items. In these approaches, different(More)
One key to cross-language information retrieval is how to efficiently resolve the translation ambiguity of queries given their short length. This problem is even more challenging when only bilingual dictionaries are available, which is the focus of this paper. In the previous research of cross-language information retrieval using bilingual dictionaries, the(More)
In a multimodal human-machine conversation, user inputs are often abbreviated or imprecise. Simply fusing multimodal inputs together may not be sufficient to derive a complete understanding of the inputs. Aiming to handle a wide variety of multimodal inputs, we are building a context-based multimodal interpretation framework called MIND (Multimodal(More)
Previous studies have shown that, in multimodal conversational systems, fusing information from multiple modalities together can improve the overall input interpretation through mutual disambiguation. Inspired by these findings, this paper investigates non-verbal modalities, in particular deictic gesture, in spoken language processing. Our assumption is(More)
One major bottleneck in conversational systems is their incapability in interpreting unexpected user language inputs such as out-of-vocabulary words. To overcome this problem, conversational systems must be able to learn new words automatically during human machine conversation. Motivated by psycholin-guistic findings on eye gaze and human language(More)