500,274 research outputs found
Are distributional representations ready for the real world? Evaluating word vectors for grounded perceptual meaning
Distributional word representation methods exploit word co-occurrences to
build compact vector encodings of words. While these representations enjoy
widespread use in modern natural language processing, it is unclear whether
they accurately encode all necessary facets of conceptual meaning. In this
paper, we evaluate how well these representations can predict perceptual and
conceptual features of concrete concepts, drawing on two semantic norm datasets
sourced from human participants. We find that several standard word
representations fail to encode many salient perceptual features of concepts,
and show that these deficits correlate with word-word similarity prediction
errors. Our analyses provide motivation for grounded and embodied language
learning approaches, which may help to remedy these deficits.Comment: Accepted at RoboNLP 201
Effective pattern discovery for text mining
Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance
Individual differences in the perception of similarity and difference.
Thematically related concepts like coffee and milk are judged to be more similar than thematically unrelated concepts like coffee and lemonade. We investigated whether thematic relations exert a small effect that occurs consistently across participants (i.e., a generalized model), or a large effect that occurs inconsistently across participants (i.e., an individualized model). We also examined whether difference judgments mirrored similarity or whether these judgments were, in fact, non-inverse. Five studies demonstrated the necessity of an individualized model for both perceived similarity and difference, and additionally provided evidence that thematic relations affect similarity more than difference. Results suggest that models of similarity and difference must be attuned to large and consistent individual variability in the weighting of thematic relations
ASR error management for improving spoken language understanding
This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic information system. An approach is proposed for enriching the set of
semantic labels with error specific labels and by using a recently proposed
neural approach based on word embeddings to compute well calibrated ASR
confidence measures. Experimental results are reported showing that it is
possible to decrease significantly the Concept/Value Error Rate with a state of
the art system, outperforming previously published results performance on the
same experimental data. It also shown that combining an SLU approach based on
conditional random fields with a neural encoder/decoder attention based
architecture , it is possible to effectively identifying confidence islands and
uncertain semantic output segments useful for deciding appropriate error
handling actions by the dialogue manager strategy .Comment: Interspeech 2017, Aug 2017, Stockholm, Sweden. 201
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Efficacy of Elaborated Semantic Features Analysis in Aphasia: a quasi-randomised controlled trial
Background: Word finding difficulty is one of the most common features of aphasia. Semantic Features Analysis (SFA) directly aims to improve word finding in people with aphasia. Evidence from systematic reviews suggests that SFA leads to positive outcomes, yet the evidence comprises single case studies and case series. There is a need to evaluate the efficacy of SFA in controlled group studies/trials.
Aims: To evaluate the efficacy of Elaborated Semantic Feature Analysis (ESFA) for word finding in people with aphasia. We investigated: (a) the efficacy of ESFA versus a delayed therapy/control, (b) the efficacy of two therapy approaches– individual versus a combination of individual and group therapy.
Methods and procedures: We ran a multi-centre, quasi-randomised controlled trial, nested in a larger study (Thales-Aphasia). Participants were recruited from community settings. They had to be people with aphasia due to stroke at least four months post-onset. Participants were randomized to individual vs combination vs delayed therapy/control groups. Both therapy groups had three hours of ESFA per week for 12 weeks. Delayed therapy/control group had no intervention for 12 weeks and were then randomized to either individual or combination therapy. The primary outcome was confrontation naming. Secondary outcomes were the Boston Naming Test, Discourse, the Functional Assessment of Communication Skills for adults (ASHA–FACS), the Stroke and Aphasia Quality of Life scale (SAQOL-39g), the General Health Questionnaire-12 item, and the EQ-5D.
Outcomes and Results: Of the 72 participants of the Thales-Aphasia project, 58 met eligibility criteria for speech-language therapy and 39 were allocated to ESFA. The critical p-value was adjusted for multiple comparisons (.005). For the therapy versus control comparison, there was a significant main effect of time on the primary outcome (p<.001, η2p=.42) and a significant interaction effect (p=.003, η2p=.21). An interaction effect for the SAQOL-39g (p=.015, η2p=.11) and its psychosocial domain (p=.013, η2p=.12) did not remain significant after Bonferroni adjustment. For the individual versus combination ESFA comparison, there were significant main effects of time on the primary outcome (p<.001, η2p=.49), the BNT (p<.001, η2p=.29) and the ASHA-FACS (p=.001, η2p=.18). Interaction and group effects were not significant.
Conclusion: Though underpowered, this study provides evidence on the efficacy of ESFA to improve word finding in aphasia, with gains similar in the two therapy approaches.
Trial registration: ISRCTN71455409, https://doi.org/10.1186/ISRCTN7145540
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