Recall Of Keywords Research Articles

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets.

People's lives are influenced by social media. It is an essential source for sharing news, awareness, detecting events, people's interests, etc. Social media covers a wide range of topics and events to be discussed. Extensive work has been published to capture the interesting events and insights from datasets. Many techniques are presented to detect events from social media networks like Twitter. In text mining, most of the work is done on a specific dataset, and there is the need to present some new datasets to analyse the performance and generic nature of Topic Detection and Tracking methods. Therefore, this paper publishes a dataset of real-life event, the Oscars 2018, gathered from Twitter and makes a comparison of soft frequent pattern mining (SFPM), singular value decomposition and k-means (K-SVD), feature-pivot (Feat-p), document-pivot (Doc-p), and latent Dirichlet allocation (LDA). The dataset contains 2,160,738 tweets collected using some seed words. Only English tweets are considered. All of the methods applied in this paper are unsupervised. This area needs to be explored on different datasets. The Oscars 2018 is evaluated using keyword precision (K-Prec), keyword recall (K-Rec), and topic recall (T-Rec) for detecting events of greater interest. The highest K-Prec, K-Rec, and T-Rec were achieved by SFPM, but they started to decrease as the number of clusters increased. The lowest performance was achieved by Feat-p in terms of all three metrics. Experiments on the Oscars 2018 dataset demonstrated that all the methods are generic in nature and produce meaningful clusters.

Evolutionary game theoretical on-line event detection over tweet streams

Current Online Social Networks represent a means for the continuous generation and distribution of information, which is slightly changed when moving from a user to another during the traversing of the network. Such an amount of information can overcome the capacity of a single user to manage it, so it would be useful to reduce it so that the user is able to have a summary of the information flowing the network. To this aim, it is of crucial importance to detect events within such an information stream, composing of the most representative words containing in each information instance, representing the event described by the set of tweet categorized together. There is a vast literature on off-line event detection on data-sets acquired from online social networks, but a similar solid set of approaches is missing if the detection has to be done on-line, which is demanding by the current applications. The driving idea described in this paper is to realize on-line clustering of tweets by leveraging on evolutionary game theory and the replicator dynamics, which have been used with success in many classification problems and/or multiobjective optimizations. We have adapted and enhanced a evolutionary clustering from the literature to meet the needs of on-line tweet clustering. Such a solution has been implemented according to the Kappa architectural model and assessed against state-of-the art approaches showing higher values of topic and keyword recall on two realistic data-sets.

Dynamic windowing mechanism to combine sentiment and N-gram analysis in detecting events from social media

Social sensing is a new paradigm that inherits the main ideas of sensor networks and considers the users as new sensor types. For instance, by the time the users find out that an event has happened, they start to share the related posts and express their feelings through the social networks. Consequently, these networks are becoming a powerful news media in a wide range of topics. Existing event detection methods mostly focus on either the keyword burst or sentiment of posts, and ignore some natural aspects of social networks such as the dynamic rate of arriving posts. In this paper, we devised Dynamic Social Event Detection approach that exploits a new dynamic windowing method. Besides, we add a mechanism to combine the sentiment of posts with the keywords burst in the dynamic windows. The combination of sentiment analysis and the frequently used keywords enhances our approach to detect events with a different level of user engagement. To analyze the behavior of the devised approach, we use a wide range of metrics including histogram of window sizes, sentiment oscillations of posts, topic recall, keyword precision, and keyword recall on two benchmarked datasets. One of the significant outcomes of the devised method is the topic recall of 100% for FA Cup dataset.

Effect of hospital noise on patients' ability to hear, understand, and recall speech

Speech intelligibility and recall were examined in normally hearing and hearing-impaired hospitalized patients. Fifty-two participants completed testing in a sound-attenuated booth. While listening to a recorded male speaker talking at conversational level, participants were asked to identify and remember the last (key) word in each of a series of five sentences presented in hospital noise with or without voices at three decibel levels (59, 64, and 69 dBA). Noise level and sentence context had the largest impact on key word identification (p < .001). Noise level had the largest impact on key word recall (p < .001). Type of hospital noise and hearing loss also significantly influenced performance on both measures. These findings have implications for healthcare providers communicating with hospitalized patients.

Effects of aging on the ability to benefit from prior knowledge of message content in masked speech recognition

Under conditions in the presence of competing talkers, presenting the early part of a target sentence in quiet improves recognition of the last keyword of the sentence. This content-priming effect depends on a working-memory resource holding the information of the early presented part of the target speech (the content prime). Older adults usually exhibit declined working memory and experience more difficulties in speech recognition under “cocktail-party” conditions. This study investigated whether speech masking also affects recall of the content prime and whether the content-priming effect declines in older adults. The results show that in both younger adults and older adults, although the content prime was heard in quiet, recall of keywords in the prime was significantly affected by the signal-to-masker ratio of the target/masker presentation. The vulnerability of prime recall to speech masking was larger in older adults than that in younger adults. Also, the content-priming effect disappeared in older adults, even though older adults are able to use the content prime to determine the target speech in the presence of competing talkers. Thus, a speech masker affects not only recognition but also recall of speech, and there is an age-related decline in both content-priming-based unmasking of the target speech and recall of the prime.

Tacit Extraction for Keyword in Chinese

The present Extraction for Keyword in Chinese (EKC for short) is merely directed against explicit keywords or prototype keywords, and has not take into account those tacit keywords distorted by network hackers with the method of active jamming in Chinese. For this purpose, with the help of M. Polanyi’s theory of tacit knowledge, this paper presents a new approach for the tacit EKC (TEKC for short), which can improve the ratio of precision and recall for information filtering. Based on the TEKC, the paper presents a set of classifications of how to distort the explicit keywords and the solutions to calculate the tacit distortion of those tacit keywords. Furthermore, 4 algorithms were designed, including in picture tacit, textspeak tacit, fake paleography tacit and character tacit, which can extract the tacit keywords in text but traditional EKC could not. Owing to the increased number of extracted keywords, the recall of keywords raised and the precision of information filtering improved. Experiments show that the classification of tacit keywords in Chinese, the calculation of tacit distortion and the algorithms to tacit extract the keywords in Chinese, etc can effectively improve the performance of EKC and raise the recall of web filtering algorithms based on TEKC.

Structure and strategy in the associative false memory paradigm

List-learning experiments can have several levels of structure: individual words, the gist (if any) of each list, and the task in which those lists are embedded. The usual presentation of the DRM associative paradigm (Deese, 1959; Roediger & McDermott, 1995) strongly encourages a focus on gist and produces a high rate of false recall of key words (FRK). The experiments reported here were designed to invite the use of memory strategies based on structures other than the gist and thus reduce FRK. The crucial condition of Experiment 1, short lists followed by rehearsal, encouraged a focus on individual words and produced a low rate of FRK. In Experiment 2, the lists were embedded in a guessing game, which virtually eliminated FRK. FRK was also low in Experiments 3a and 3b when participants engaged in a complex task involving the first letters of list words. The relevance of these findings to false memories in the DRM and the connection of false autobiographical memories is discussed.

Learning during general anaesthesia: Implicit recall of key words presented during cardiac anaesthesia

Learning during general anaesthesia: Implicit recall of key words presented during cardiac anaesthesia

Recall of Key Words as a Function of Bloom's Cognitive Level

ABSTRACT Two experiments were designed to pursue the levels of processing analysis of Bloom's taxonomy of the cognitive domain proposed by Kunen, Cohen & Solman (1981). In the first experiment four groups of students processed 20 principles at the levels of knowledge, application, synthesis and evaluation respectively. The times required by students in the first two of these groups were significantly shorter than the times required by the last two groups, but in conrast to the Kunen et al. study, no mathematical problems were included to correct for these differences. The percentages of correctly recalled principle names showed the same pattern of differences as previously obtained by Kunen et al. for their Australian sample and it was concluded that the interpolated problems used by these authors had not interfered with performance. However, since the levels of performance did not differ for application and synthesis (although they were superior to the levels obtained for knowledge and evaluation), the r...