Unsupervised word sense disambiguation for Korean through the acyclic weighted digraph using corpus and dictionary

Word sense disambiguation (WSD) is meant to assign the most appropriate sense to a polysemous word according to its context. We present a method for automatic WSD using only two resources: a raw text corpus and a machine-readable dictionary (MRD). The system learns the similarity matrix between word pairs from the unlabeled corpus, and it uses the vector representations of sense definitions from MRD, which are derived based on the similarity matrix. In order to disambiguate all occurrences of polysemous words in a sentence, the system separately constructs the acyclic weighted digraph (AWD) for every occurrence of polysemous words in a sentence. The AWD is structured based on consideration of the senses of context words which occur with a target word in a sentence. After building the AWD per each polysemous word, we can search the optimal path of the AWD using the Viterbi algorithm. We assign the most appropriate sense to the target word in sentences with the sense on the optimal path in the AWD. By experiments, our system shows 76.4% accuracy for the semantically ambiguous Korean words.     

Optimization of some factors affecting the performance of query expansion

This paper examines the factors affecting the performance of global query expansion based on term co-occurrence data and suggests a way to maximize the retrieval effectiveness. Major parameters to be optimized through experiments are term similarity measure and the weighting scheme of additional terms. The evaluation of four similarity measures tested in query expansion reveal that mutual information and Yule's Y, which emphasize low frequency terms, achieve better performance than cosine and Jaccard coefficients that have the reverse tendency. In the evaluation of three weighting schemes, similarity weight performs well only with short queries, whereas fixed weights of approximately 0.5 and similarity rank weights were effective with queries of any length. Furthermore, the optimal similarity rank weight achieving the best overall performance seems to be the least affected by test collections and the number of additional terms. For the efficiency of retrieval, the number of additional terms needs not exceed 70 in our test collections, but the optimal number may vary according to the characteristics of the similarity measure employed.     

Parsimonious translation models for information retrieval

In the KL divergence framework, the extended language modeling approach has a critical problem of estimating a query model, which is the probabilistic model that encodes the user’s information need. For query expansion in initial retrieval, the translation model had been proposed to involve term co-occurrence statistics. However, the translation model was difficult to apply, because the term co-occurrence statistics must be constructed in the offline time. Especially in a large collection, constructing such a large matrix of term co-occurrences statistics prohibitively increases time and space complexity. In addition, reliable retrieval performance cannot be guaranteed because the translation model may comprise noisy non-topical terms in documents. To resolve these problems, this paper investigates an effective method to construct co-occurrence statistics and eliminate noisy terms by employing a parsimonious translation model. The parsimonious translation model is a compact version of a translation model that can reduce the number of terms containing non-zero probabilities by eliminating non-topical terms in documents. Through experimentation on seven different test collections, we show that the query model estimated from the parsimonious translation model significantly outperforms not only the baseline language modeling, but also the non-parsimonious models.     

An empirical study of query expansion and cluster-based retrieval in language modeling approach

The term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on query expansion and cluster-based retrieval. We examine the effect of using parsimony in query expansion and the effect of clustering algorithms in cluster-based retrieval. In addition, query expansion and cluster-based retrieval are compared, and their combinations are evaluated in terms of retrieval performance by performing experimentations on seven test collections of NTCIR and TREC.     

An Expanded View of Joint Attention: Skill,Engagement, and Language in Typical Development and Autism

This study provides an expanded view of joint attention and its relation to expressive language development. A total of 144 toddlers (40 typically developing, 58 with autism spectrum disorder [ASD], 46 with developmental delay [DD]) participated at 24 and 31 months. Toddlers who screened positive for ASD risk, especially those subsequently diagnosed with ASD, had poorer joint attention skills, joint engagement during parent–toddler interaction, and expressive language. Findings highlight the dynamic relation between joint attention and language development. In the ASD and DD groups, joint engagement predicted later expressive vocabulary, significantly more than predictions based on joint attention skills. Joint engagement was most severely impacted when toddlers did not talk initially and improved markedly if they subsequently began to speak.     

Religious Development in African American Adolescents: Growth Patterns That Offer Protection

Although religiosity promotes resilient outcomes in African American (AA) adolescents, there is a lack of research that examines the protective role of religious development in the context of stressful life events (SLEs). In 1,595 AA adolescents, a cohort-sequential design was used from ages 12 to 18 to examine subtypes of religiosity, as well as distinct developmental patterns within subtypes. The protective role of religious development was also examined in the context of SLEs. The study findings indicated two subtypes of religiosity—extrinsic and intrinsic religiosity—as well as low and high developmental patterns in the identified subtypes. Furthermore, the protective influence of extrinsic and intrinsic religiosity gradually diminished from age 12 to 18 in the context of SLEs.     

An Entity Theory of Intelligence Predicts Higher Cortisol Levels When High School Grades Are Declining

Grades often decline during the high school transition, creating stress. The present research integrates the biopsychosocial model of challenge and threat with the implicit theories model to understand who shows maladaptive stress responses. A diary study measured declines in grades in the first few months of high school: salivary cortisol (N = 360 students, N = 3,045 observations) and daily stress appraisals (N = 499 students, N = 3,854 observations). Students who reported an entity theory of intelligence (i.e., the belief that intelligence is fixed) showed higher cortisol when grades were declining. Moreover, daily academic stressors showed a different lingering effect on the next day's cortisol for those with different implicit theories. Findings support a process model through which beliefs affect biological stress responses during difficult adolescent transitions.     

Development as education for social justice

Asia Pacific Education Review - The correlation between education and development, with time, went through many changes along with the changes of perspectives. Since both of these concepts...     

Proposing an Effective Action Learning Process Model: Introducing the PAIR Model

Action learning differentiates itself from other inquiry‐based learning approaches in that it requires learners to take actions based on project outcomes. While implementing their plans and reflecting on consequences of their actions, learners are given more and varied learning opportunities. Despite the apparent benefits of action learning, it entails a more complex process and a higher level of involvement from top management than do ordinary training programs. This article seeks to identify benefits and limitations of action learning and proposes a process model that enhances its benefits and helps to overcome its limitations. Thirty‐one research studies from academic journals were reviewed to identify espoused benefits and limitations of action learning. Those findings were used as the foundation for development of the PAIR model.