Interaction between semantic and phonological processes in stuttering Evidence from the dual-task paradigm

Stuttering is a common neurological deficit and its underlying cognitive mechanisms are a matter of debate, with evidence suggesting abnormal modulation between speech encoding and other cognitive components. Previous studies have mainly used single task experiments to investigate the disturbance of language production. It is unclear whether there is abnormal interaction between the three language tasks （orthographic, phonological and semantic judgment） in stuttering patients. This study used dual tasks and manipulated the stimulus onset asynchrony （SOA） between tasks 1 and 2 and the nature of the second task, including orthographic, phonological, and semantic judgments. The results showed that the performance records of orthographic judgment, phonological judgment, and semantic judgment were significantly reduced between the patient and control groups with short SOA （P 〈 0.05）. However, different patterns of interaction between task 2 and SOA were observed across subject groups： subjects with stuttering were more strongly modulated by SOA when the second task was semantic judgment or phonological judgment （P 〈 0.05）, but not in the orthographic judgment experiment （P 〉 0.05）. These results indicated that the interaction mechanism between semantic processing and phonological encoding might be an underlying cause for stuttering.

Marine spatio-temporal process semantics and its applications-taking the El Nio Southern Oscilation process and Chinese rainfall anomaly as an example

Spatio-temporal semantics based on ＂object views＂ or ＂event views＂ has few abilities to represent and model the continuity and gradual oceanic phenomena or objects, which seriously limits the specific marine applications and knowledge discovery and data mining, so this paper proposes a hierarchical abstraction semantics with ＂marine spatio-temporal process-life span phases-evolution sequences--state units＂ and process objects included by level with ＂marine process objects--phase objects--sequence object---state objects＂ with the oceanic process characteristics into the marine process semantics. In addition, this paper designs the storage and representation of marine process objects using the backus normal forms （BNF） and abstract data type （ADT）. Base on E1 Nifio Southern Oscilation （ENSO） index and Chinese rain gauging station data, this paper also gives a case of study. The spatio-temporal analysis between ENSO process and Chinese rainfall anomalies shows that the marine spatio-temporal semantics not only can illustrate the spatial distribution of Chinese rainfall anomalies in different time scales at ENSO process, life span phases and state units, but also analyze the dynamic changes of Chinese rainfall anomalies in different life span phases or state units within ENSO evolution.

Contrastive analysis of prefix dis- and un- in English

English prefix dis- and tin- are commonly surveyed along with some other negative prefixes. However, there are many cases in which prefix dis- and un- can be attached to the same root, causing the difficulty in distinguishing the comprehension of the two prefixes＇ semantic roles. Considering this issue, this paper endeavors to clarify the respective morphological processes and explain their specific semantic roles.

A Multiple Feature Approach for Disorder Normalization in Clinical Notes

In this paper we propose a multiple feature approach for the normalization task which can map each disorder mention in the text to a unique unified medical language system（UMLS）concept unique identifier（CUI）. We develop a two-step method to acquire a list of candidate CUIs and their associated preferred names using UMLS API and to choose the closest CUI by calculating the similarity between the input disorder mention and each candidate. The similarity calculation step is formulated as a classification problem and multiple features（string features,ranking features,similarity features,and contextual features） are used to normalize the disorder mentions. The results show that the multiple feature approach improves the accuracy of the normalization task from 32.99% to 67.08% compared with the Meta Map baseline.