Distributional Sentence Entailment Using Density Matrices

Categorical compositional distributional model of Coecke et al. (2010) suggests a way to combine grammatical composition of the formal, type logical models with the corpus based, empirical word representations of distributional semantics. This paper contributes to the project by expanding the model to also capture entailment relations. This is achieved by extending the representations of words from points in meaning space to density operators, which are probability distributions on the subspaces of the space. A symmetric measure of similarity and an asymmetric measure of entailment is defined, where lexical entailment is measured using von Neumann entropy, the quantum variant of Kullback-Leibler divergence. Lexical entailment, combined with the composition map on word representations, provides a method to obtain entailment relations on the level of sentences. Truth theoretic and corpus-based examples are provided.Comment: 11 page

Photoacoustic ultrasound sources from diffusion-limited aggregates

Metallic diffusion-limited aggregate (DLA) films are well-known to exhibit near-perfect broadband optical absorption. We demonstrate that such films also manifest a substantial and relatively material-independent photoacoustic response, as a consequence of their random nanostructure. We theoretically and experimentally analyze photoacoustic phenomena in DLA films, and show that they can be used to create broadband air- coupled acoustic sources. These sources are inexpensive and simple to fabricate, and work into the ultrasonic regime. We illustrate the device possibilities by building and testing an optically-addressed acoustic phased array capable of producing virtually arbitrary acoustic intensity patterns in air.Comment: 5 pages, 5 figure

Sentence entailment in compositional distributional semantics

Distributional semantic models provide vector representations for words by gathering co-occurrence frequencies from corpora of text. Compositional distributional models extend these from words to phrases and sentences. In categorical compositional distributional semantics, phrase and sentence representations are functions of their grammatical structure and representations of the words therein. In this setting, grammatical structures are formalised by morphisms of a compact closed category and meanings of words are formalised by objects of the same category. These can be instantiated in the form of vectors or density matrices. This paper concerns the applications of this model to phrase and sentence level entailment. We argue that entropy-based distances of vectors and density matrices provide a good candidate to measure word-level entailment, show the advantage of density matrices over vectors for word level entailments, and prove that these distances extend compositionally from words to phrases and sentences. We exemplify our theoretical constructions on real data and a toy entailment dataset and provide preliminary experimental evidence.Comment: 8 pages, 1 figure, 2 tables, short version presented in the International Symposium on Artificial Intelligence and Mathematics (ISAIM), 201

Health-related quality of life and patient reports about care outcomes in a multidisciplinary hospital intervention

Patient perceptions of care and health-related quality of life (HRQOL) are important out comes for hospitalized patients. Purpose: This study examined patient experiences with hospital care and HRQOL in individuals hospitalized at a west coast teaching hospital. Methods: We assessed patient experiences with care and HRQOL using interviews with 1,207 hospitalized, general medicine patients participating in a multi-disciplinary provider team intervention at a large academic medical center Patient outcome variables included the Picker dimensions of hospital care (Continuity and Transition, Coordination of Care, Emotional Support, Information and Education, Involvement of Family and Friends, Physical Comfort, Respect for Patient Preferences, Overall Impression), the Health Utilities Index Mark 3 (HUI-3), and the SF-12 physical (PCS-12) and mental health (MCS-12) summary scores. Results: Patients randomized to a multidisciplinary intervention reported higher emotional support (b = 3.32), t(903) = 2.01, p =.044, and physical comfort (b = 3.49), t(863) = 2.25, p =.025,from health care providers than did the control group, but these effects became nonsignificant after adjusting for multiple comparisons. The HUI-3, PCS-12, and MCS-12 summary scores improved significantly from baseline to the 30-day, ts(943, 919, 860) = 4.94, 2.20, and 5.31, ps <.0001, =.03, and <.0001, respectively, and the 4-month follow-ups, ts(871, 919, 943) = 7.25, 8.68, and 8.08, ps <. 001, <. 001, and <. 0001, respectively, but change on these measures did not differ berween intervention and control patients. Baseline health was significantly associated with patient evaluations of hospital care, but patient evaluations did not predict future health. Conclusions: There were no differences in reports and ratings of hospital care or HRQOL between the control and the intervention groups. Hence, the behavioral changes in hospital staff in the intervention group had no effect on patient-reported outcomes. Mental health at baseline was predictive of patient evaluations of the hospitalization, but evaluations of care were not associated with subsequent HRQOL. Thus, it may be important to adjust patient evaluations of hospital care for case-mix differences in health

The Recognizing Textual Entailment Challenges: Datasets and Methodologies

While semantic inference has always been a major focus in Computational Linguistics, the topic has benefited of new attention in the field thanks to the Recognizing Textual Entailment (RTE) framework, first launched in 2004, which has provided an operational definition of entailment based on human judgements over portions of text. On top of such definition, a task has been designed, which includes both guidelines for dataset annotation and evaluation metrics for assessing systems' performance. This chapter presents the successful experience of creating Textual Entailment datasets. We show how, during the years, RTE datasets have been developed in several variants, not only to address complex phenomena underlying entailment, but also to demonstrate the potential application of entailment inference into concrete scenarios, including summarization, knowledge base population, answer validation for question answering, and student answer assessment

Learning Lexical-Semantic Relations Using Intuitive Cognitive Links

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