A Study On Assessment Of Stress And Its Management In Hospital Employess

Stress refers to the strain from the conflict between our external environment and us, leading to emotional and physical pressure. In our fast paced world, it is impossible to live without stress, whether you are a student or a working adult. There is both positive and negative stress, depending on each individual’s unique perception of the tension between the two forces. Stress bears deliberating effects on both the employees and the employer. As per Dr H seyles, Stress is defined as “a state of psychological and physiological imbalance resulting from the disparity between situational demand and the individual’s ability and motivation to meet those needs”. The word stress has its origin in the Latin word string ere to draw tight. In the 17thcentury the word was used to describe affliction and hardship. The meaning of the word later included the concepts of pressure, strain or force. Today the description of stress includes an outside stimulus and the person’s response to it. Several studies have focused on the possible relation between stress, illness and different ways people respond to it. These studies distinguish the various aspects of stress which a person may face in life, e.g. stress at home, in personal life or at work. This review focuses on stress at work, with particular emphasis to the nursing profession, in an attempt to explore possible management strategies that may decrease it (Golubic etal., 2009, Lu et al. 2009).

Detecting new Chinese words from massive domain texts with word embedding

Textual information retrieval (TIR) is based on the relationship between word units. Traditional word segmentation techniques attempt to discern the word units accurately from texts; however, they are unable to appropriately and efficiently identify all new words. Identification of new words, especially in languages such as Chinese, remains a challenge. In recent years, word embedding methods have used numerical word vectors to retain the semantic and correlated information between words in a corpus. In this article, we propose the word-embedding-based method (WEBM), a novel method that combines word embedding and frequent n-gram string mining for discovering new words from domain corpora. First, we mapped all word units in a domain corpus to a high-dimension word vector space. Second, we used a frequent n-gram word string mining method to identify a set of candidates for new words. We designed a pruning strategy based on the word vectors to quantify the possibility of a word string being a new word, thereby allowing the evaluation of candidates based on the similarity of word units in the same string. In a comparative study, our experimental results revealed that WEBM had a great advantage in detecting new words from massive Chinese corpora.

Punctuation Generation Inspired Linguistic Features for Mandarin Prosody Generation

This paper proposes two fully-automatic machine-extracted linguistic features from an unlimited text input for Mandarin prosody generation. One is the punctuation confidence (PC) which measures the likelihood of inserting a major punctuation mark (PM) at a word boundary. Another is the quotation confidence (QC) which measures the likelihood of a word string to be quoted as a meaningful or emphasized unit in text. Because a major PM in a text is highly correlated with a prosodic break, and a quoted word string plays an important role in human language understanding, the two features potentially could provide useful information for prosody generation. The idea is first realized by employing conditional random field (CRF)-based models to predict major PMs, quoted word string locations, and their associated confidences, i.e., the PC and the QC, for each word boundary. Then, the predicted punctuations and their confidences are combined with traditional contextual linguistic features to predict prosodic-acoustic features. Both objective and subjective tests showed that the prosody generation with the proposed linguistic features performed better than the one without the proposed features. So, the proposed PC and QC are promising features for Mandarin prosody generation.

A Method for Chinese Noun Phrase Recognition Base on Word Co-Occurrence Directed Graph

This paper proposes a recognition method for Chinese Noun Phrase based on word co-occurrence directed graph. An input document is firstly scanned in which noun word string is retrieved. Atomic word table and word co-occurrence directed graph is then generated according to the word strings. A search is performed on the graph to find the longest paths with priority weight satisfying certain criteria. The word strings corresponding to the paths are considered as noun phrases. As dimensionality reduction is applied, the scale of the word co-occurrence directed graph is reduced significantly, and thus the efficiency of the algorithm is improved. Experimental results demonstrate that the precision of noun phrase recognition reaches 95.4%.

Ratio Frequency: Insights into Usage Effects on Phonological Structure from Hiatus Resolution in New Mexican Spanish

Mounting evidence shows that frequency of use plays a fundamental role in shaping linguistic structure, including phonological structure (cf. Bybee 2001). Because the study of frequency effects is relatively new, our understanding of how they impact structure continues to be refined. This study explores the effects of several frequency measures on the resolution of hiatus between words in Spanish, and reveals that in addition to the traditional phonological factors, frequency is also involved. Multivariate analyses show that ratio frequency - or the frequency of a two-word string relative to that of one of the words it contains - is a better indicator than straightforward token frequency of the likelihood that the string will be processed as an autonomous unit and undergo concurrent phonological reduction. These findings build on a usage-based model of language, providing important insights into the nature of lexical storage and how this relates to linguistic variation and change.