Using Gestures to Resolve Lexical Ambiguity in Storytelling with Humanoid Robots

Gestures that co-occur with speech are a fundamental component of communication. Prior research with children suggests that gestures may help them to resolve certain forms of lexical ambiguity, including homophones. To test this idea in the context of human-robot interaction, the effects of iconic and deictic gestures on the understanding of homophones was assessed in an experiment where a humanoid robot told a short story containing pairs of homophones to small groups of young participants, accompanied by either expressive gestures or no gestures. Both groups of subjects completed a pretest and post-test to measure their ability to discriminate between pairs of homophones and we calculated aggregated precision. The results show that the use of iconic and deictic gestures aids in general understanding of homophones, providing additional evidence for the importance of gesture to the development of children’s language and communication skills

The biomedical discourse relation bank

<p>Abstract</p> <p>Background</p> <p>Identification of discourse relations, such as causal and contrastive relations, between situations mentioned in text is an important task for biomedical text-mining. A biomedical text corpus annotated with discourse relations would be very useful for developing and evaluating methods for biomedical discourse processing. However, little effort has been made to develop such an annotated resource.</p> <p>Results</p> <p>We have developed the Biomedical Discourse Relation Bank (BioDRB), in which we have annotated explicit and implicit discourse relations in 24 open-access full-text biomedical articles from the GENIA corpus. Guidelines for the annotation were adapted from the Penn Discourse TreeBank (PDTB), which has discourse relations annotated over open-domain news articles. We introduced new conventions and modifications to the sense classification. We report reliable inter-annotator agreement of over 80% for all sub-tasks. Experiments for identifying the sense of explicit discourse connectives show the connective itself as a highly reliable indicator for coarse sense classification (accuracy 90.9% and F1 score 0.89). These results are comparable to results obtained with the same classifier on the PDTB data. With more refined sense classification, there is degradation in performance (accuracy 69.2% and F1 score 0.28), mainly due to sparsity in the data. The size of the corpus was found to be sufficient for identifying the sense of explicit connectives, with classifier performance stabilizing at about 1900 training instances. Finally, the classifier performs poorly when trained on PDTB and tested on BioDRB (accuracy 54.5% and F1 score 0.57).</p> <p>Conclusion</p> <p>Our work shows that discourse relations can be reliably annotated in biomedical text. Coarse sense disambiguation of explicit connectives can be done with high reliability by using just the connective as a feature, but more refined sense classification requires either richer features or more annotated data. The poor performance of a classifier trained in the open domain and tested in the biomedical domain suggests significant differences in the semantic usage of connectives across these domains, and provides robust evidence for a biomedical sublanguage for discourse and the need to develop a specialized biomedical discourse annotated corpus. The results of our cross-domain experiments are consistent with related work on identifying connectives in BioDRB.</p

The interaction of knowledge sources in word sense disambiguation

Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in artificial in telligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results. We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94% on our evaluation corpus.Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems

Misunderstanding and the Negotiation of Meaning Using Abduction

this paper, we will consider previous work on misunderstanding. Then, we will overview the architecture of new model and see how it accounts for an example repair

The Need to Address Plan Misinference During Dialogues and Why Abduction Might Help

ion rename by copy oe rename file Figure 1: An example planning theory Speech act understanding [ Hinkelman, 1990 ] treats different features in the input, such as the mood of a sentence or the presence of a particular lexical item, as manifestations of different speech acts. For example, &quot;please&quot; is a manifestation of a request. The system matches features against the input to determine a set of candidates, which are then filtered on the basis of the consistency of their implicatures (similar to Allen&apos;s inference rules) with a model of prior beliefs. [ Traum and Hinkelman, 1992 ] extend this work, generalizing the notion of speech act to conversation acts. These acts include the taking and releasing of turns, and the initiating, clarifying, or acknowledging of an utterance. Unlike speech acts, conversation acts require some positive evidence by the listener before they are accepted as understood. To provide an additional filter on candidate interpretations, the acts have been organiz..