Simulating Code-switching Using a Neural Network Model of Bilingual Sentence Production

A Computational Neural Network Model for College English Grammar Correction

For the error correction of English grammar, if there are errors in the semantic units (words and sentences), it will inevitably affect the subsequent text analysis and semantic understanding, and ultimately reduce the overall performance of the practical application system. Therefore, intelligent error detection and correction of the word and grammatical errors in English texts is one of the key and difficult points of natural language processing. This exploration innovatively combines a computational neural model with college grammar error correction to improve the accuracy of college grammar error correction. It studies the computational neural model in English grammar error correction based on a neural network named Knowledge and Neural machine translation powered College English Grammar Typo Correction (KNGTC). First, the Recurrent Neural Network is introduced, and the overall structure of the English grammatical error correction neural model is constructed. Moreover, the supervised training of Attention is discussed, and the experimental environment and experimental data are given. The results show that KNGTC has high accuracy in college English grammar correction, and the accuracy of this model in CET-4 and CET-6 writing can reach 82.69%. The English grammar error correction model based on the computational neural network has perfect function and strong error correction ability. The optimization and perfection of the model can improve students' English grammar level, which has certain practical value. After years of continuous optimization and improvement, English grammar error correction technology has entered a performance bottleneck. This mode's construction can break the current technology's limitations and bring a better user experience. Therefore, it is very valuable to study the error correction model of English grammar in practical application.

A Neural Network Model of Smart Aging Combining Family Structure Change Factors

In this paper, we analyze the changes in family structure and explore the changes in detail, based on which we construct a neural network model of smart aging. Based on the gender perspective, the individual growth model in the multilayer linear model is used to examine the effects of family structure changes on the elderly in terms of economic exchange, daily care, and emotional support. The results show that there is no significant gender difference in the family structure changes on the elderly in terms of economic exchange and daily care, but there is a significant gender difference in terms of emotional support. To solve the problem of data imbalance in the daily activity categories of the elderly, this paper resamples the data and uses different neural network models for activity recognition of the sensor data generated from the daily activities of the elderly. In this paper, the daily behavior patterns of the elderly over a while are studied by correlating three conditions of time distance, optimal path, and sensor distance to discover the daily behavior patterns of the elderly, while the abnormal behavior patterns can be well separated by EM clustering algorithm. The daily behavior of the elderly is a coarse-grained representation of their daily activities. It is not limited to a specific activity and does not require the sensor ID, trigger time, and location triggered by the activity to be consistent, but in long-term daily activity data, it abstracts the general behavior rules of the elderly activities. Through the research of this paper, the existing system is improved, and the multifaceted needs of the elderly are fully considered, from housing needs to spiritual needs, to face the current elderly care problems with a positive attitude, create a good social elderly care environment for the elderly, and realize the real elderly care.

Developmental effects in the online use of morphosyntactic cues in sentence processing: Evidence from Tagalog

Children must necessarily process their input in order to learn it, yet the architecture of the developing parsing system and how it interfaces with acquisition is unclear. In the current paper we report experimental and corpus data investigating adult and children's use of morphosyntactic cues for making incremental online predictions of thematic roles in Tagalog, a verb-initial symmetrical voice language of the Philippines. In Study 1, Tagalog-speaking adults completed a visual world eye-tracking experiment in which they viewed pictures of causative actions that were described by transitive sentences manipulated for voice and word order. The pattern of results showed that adults process agent and patient voice differently, predicting the upcoming noun in the patient voice but not in the agent voice, consistent with the observation of a patient voice preference in adult sentence production. In Study 2, our analysis of a corpus of child-directed speech showed that children heard more patient voice- than agent voice-marked verbs. In Study 3, 5-, 7-, and 9-year-old children completed a similar eye-tracking task as used in Study 1. The overall pattern of results suggested that, like the adults in Study 1, children process agent and patient voice differently in a manner that reflects the input distributions, with children developing towards the adult state across early childhood. The results are most consistent with theoretical accounts that identify a key role for input distributions in acquisition and language processing.