Word Clusters Research Articles

Estimate of global research trends and performance in family therapy in Social Science Citation Index

The main purpose of this study was to evaluate the global progress and quantitative assessment of current research trends on family therapy, using a bibliometric approach and exploring related literature in the Social Science Citation Index (SSCI) database from 1992 to 2009. This study used the bibliometric arrropach to learn about the subject categories, core journals, top countries and leading research institutes in publication, most frequently used author keywords, and most frequently used KeyWords Plus. Also, this study used a "word cluster analysis" method to locate research hot topics. A majority of the subject categories were located in clinical psychology and family studies. The core journals for family therapy located in Journal of Marital and Family Therapy, Contemporary Family Therapy, and Journal of Family Therapy. The US ranked as the top country of world articles with the highest h-index, followed distantly by the UK and Germany. The leading research institutes were Purdue University, University of Miami, and Brigham Young University. "Adolescents" and "adolescent" were highly used words in article titles. Next, the top three most frequently used author keywords were "anorexia nervosa", "adolescents", and "psychotherapy". Finally, the top three most frequently used KeyWords Plus were "psychotherapy", "children", and "intervention". Based on "word cluster analysis" to determine the research hotspots, the research hot topics of family therapy fall into three categories: treated subjects, treated matters, and treatment issues. The research trend in family therapy seems to involve the therapist often treating adolescents or children for eating disorders, substance abuse, depression, or schizophrenia. During treatment or therapy, therapists and researchers must pay attention to the issues of gender, training, and therapeutic alliance.

Word clusters and reformulation markers in Chinese and English

This article presents a corpus-based contrastive study of word clusters and reformulation markers in Chinese and English, and discusses the implications of the findings for translation universal hypotheses. The study is based on three balanced comparable corpora which represent British English, native Chinese and translational Chinese in addition to an English-Chinese parallel corpus which provides a basis for comparing native and translated English and investigating explicitation in translation. Our results show that word clusters are substantially more common in translated Chinese, suggesting a tendency in translations to use fixed and semi-fixed recurring patterns in an attempt to achieve improved fluency. The more frequent use of word clusters, especially those of high frequency and high coverage in translational Chinese, is also likely to be a result of the influence of the English source language because word clusters are significantly more prevalent in native English in relation to native Chinese. Chinese and English tend to use reformulation markers of different styles while on the other hand, reformulation markers are generally more common in both translated English and translated Chinese than in their native counterparts, suggesting that reformulation markers function as a strategy for explicitation in translations, which tend to use oral, stylistically simpler forms than non-translated texts.

Second Language Learning Benefits from Similarity in Word Endings: Evidence from Russian

Diminutive derivations (e.g., froggy, bootie) constitute morphophonologically similar clusters of words in child‐directed speech and serve as low‐level schema for learning morphological regularities. Previous research has demonstrated that by regularizing noun endings, diminutives (e.g., Russian: domik, svechka) facilitate word‐boundary identification and the acquisition of inflectional morphology. In this study, adult native speakers of English (N = 77) were exposed to diminutive and simplex transparently gender‐marked nouns instantiating Russian case‐marking and adjective‐noun gender‐agreement patterns, over six 1‐hour language‐learning sessions. They were subsequently tested on their ability to extend grammatical patterns to new items and vocabulary recall. Learners showed equivalent learning of the trained phrases containing diminutive and simplex nouns but were more accurate in generalizing morphological patterns to diminutive nouns. Furthermore, learners showed a diminutive advantage in vocabulary retention. By increasing the invariant parts of words, diminutive derivations may reduce the amount of phonological material to be memorized and, subsequently, enhance word learning.

Cancercontribution: an innovative example of Participatory Medicine in cancer.

If we picked out the words most frequently used at the Health-care and social media Doctors 2.0 congress, ‘Participatory Medicine’ would stand out for sure together with ‘community’ and ‘empowerment’, building up a cluster of words around the concept of the evolving and more active role of the patient in medicine through social media and e-health tools. But what exactly is Participatory Medicine? And what are concrete and current examples of its implementation in Europe? As defined by Gilles Frydman (Gilles, also at Doctors 2.0, is a pioneer of medical online communities and founder in 1995, of the Association of Cancer Online Resources, http://www.acor.org/), Participatory Medicine refers to a movement in which networked patients shift from being ‘mere passengers to responsible drivers of their health’. (http://participatorymedicine.org/) While online social media are very powerful tools for enabling this shift, they are by no means the only ones. Indeed the concept itself of Participatory Medicine precedes, and is not limited to, social media online, as it encompasses a cooperative model of health care that encourages and expects active involvement by all connected parties (patients, caregivers, healthcare professionals, researchers, policy makers) as integral to the full continuum of care. One innovative example of Participatory Medicine which relies on, but is not limited to, social media is the recently launched ‘Cancercontribution’, a project of collaborative intelligence based at the Villejuif Cancer Campus Pole Citoyen in Paris (http://www.cancercontribution.fr/). As the needs of society are constantly evolving due to the aging population, demographic changes, and a capillary diffusion of Information and Communication Technologies, the aim of Cancercontribution is to be proactive in this regard and provide a platform that, by involving first and foremost patients, caregivers and citizens, develops new participatory guidelines and political proposals to anticipate these changes. Cancercontribution stimulates debates on sensible topics, such as opening up the news about a cancer diagnosis, working with cancer, the patients’ role in clinical trials, side effects of treatments, and so on. The platform invites the users-patients to express their opinions, following the formal principle that each opinion is given the same weight as the others. The Cancercontribution agenda is based on three very fundamental points: in chronic diseases, and especially in cancer (where the tendency to chronicisation is only going to increase in the coming decades due to demographic changes and to advances in therapies), patients demand more information than the evidence-based data offered by institutional actors; An important and unique kind of expertise is brought forward by patients and caregivers’ experiences. This resource is unique in that it cannot be produced by classical experimental proceedings; Expertise can be co-built by an interactive process which involves contributors of different backgrounds, such as patients, professionals, caregivers, politicians, associations, and civil society. The collaborative expertise that is co-created by this interactive process constitutes ground to identify societal needs and health system’s weaknesses, while also leaving room for constantly improving existing practices. All the opinions and data collected are then analysed and elaborated to develop new participatory guidelines and political proposals. Such a ‘web laboratory’ is thus a tool that facilitates the exercise of citizenship and direct democracy, in so far as it gives space to each person’s opinion without the need of mediation through representatives, third parties or other associations. It is in this regard that Cancercontributuon is a unique example of a laboratory that, while being grounded in the virtual world, goes beyond that to reach out and impact on the real one. As one of the key take-home messages from Doctors 2.0 was the need for an online space where doctors, patients and caregivers can communicate, exchange information, create and co-produce knowledge, the platform created by Cancercontribution can be regarded as one important step in this direction for cancer care in Europe within the framework of Participatory Medicine.

복수 자질에 의한 지적 구조의 계량정보학적 분석연구: 국내 대학도서관 분야 연구논문을 대상으로

The purpose of this study is to identify topic areas of academic library research u: ing two informetric methods: word clustering and Pathfinder network. For the data analysis, 139 article published in major library and information science journals from 2005 to 2009 were collected from the Korean Science Citation Index database. The keywords that represent research topics were gathered from two sections: an abstract and titles in references. Results showed that reference titles usefully represent topics in detail. and combining abstracts and reference tiles can produce an expanded topic map.

Vari-Gram Language Model Based on Category

Category-based statistic language model is an important method to solve the problem of sparse data. But there are two bottlenecks about this model: (1) the problem of word clustering, it is hard to find a suitable clustering method that has good performance and not large amount of computation. (2) class based method always lose some prediction ability to adapt the text of different domain. The authors try to solve above problems in this paper. This paper presents a novel definition of word similarity. Based on word similarity, this paper gives the definition of word set similarity. Experiments show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance. At the same time, this paper presents a new method to create the vari-gram model.

It's Never Too Soon to Start Preparing for Tenure

It's Never Too Soon to Start Preparing for Tenure

Memory and Self-knowledge in Young Adults with ADHD

Memory for items encoded self-referentially was examined in college students with Attention deficit/hyperactivity disorder (ADHD). In Study 1, memory was assessed using the standard self-reference effect (SRE) paradigm—which requires participants to access semantic trait self-knowledge to perform self-referential encoding (e.g., Klein, 2004). ADHD and control groups produced virtually identical SRE profiles: (a) better recall following self-referential encoding than following either semantic or structural encoding; and (b) greater-than-chance clustering in recall of words encoded self-referentially. In Study 2 the self-referential task was altered—its performance required access to personal events in episodic memory (e.g., Klein, Loftus, & Burton, 1989). The ADHD group differed from controls in two ways: (a) they recalled significantly fewer words in the self-referential condition; and (b) their clustering of words encoded self-referentially failed to exceed chance levels. These results suggest the ADHD participants have less well-organized personal narratives than do persons without ADHD, a finding with relevance for memory and planning difficulties documented in people with ADHD.

Word Activation Forces Map Word Networks

AbstractWords associate with each other in a manner of intricate clusters^1-3^. Yet the brain capably encodes the complex relations into workable networks^4-7^ such that the onset of a word in the brain automatically and selectively activates its associates, facilitating language understanding and generation^8-10^. One believes that the activation strength from one word to another forges and accounts for the latent structures of the word networks. This implies that mapping the word networks from brains to computers^11,12^, which is necessary for various purposes^1,2,13-15^, may be achieved through modeling the activation strengths. However, although a lot of investigations on word activation effects have been carried out^8-10,16-20^, modeling the activation strengths remains open. Consequently, huge labor is required to do the mappings^11,12^. Here we show that our found word activation forces, statistically defined by a formula in the same form of the universal gravitation, capture essential information on the word networks, leading to a superior approach to the mappings. The approach compatibly encodes syntactical and semantic information into sparse coding directed networks, comprehensively highlights the features of individual words. We find that based on the directed networks, sensible word clusters and hierarchies can be efficiently discovered. Our striking results strongly suggest that the word activation forces might reveal the encoding of word networks in the brain.

Building Statistical Language Models for Persian Continuous Speech Recognition Systems Using the Peykare Corpus

In this paper, we build statistical language models for the Persian language using a Persian corpus called Peykare. Then, we incorporate the constructed language models in a Persian continuous speech recognition (CSR) system. First, we unify the different orthographies of words to make the texts of the corpus consistent. In addition, we decrease the number of POS tags used in the corpus by manual clustering. Then, the word-based and the class-based n-gram language models are built using the unified and reduced-tag-set corpus. For building the class-based language models, several methods are used including a new method called LGM-based word clustering. We present the procedure of incorporating language models in a Persian CSR system. Using these language models absolute reductions of up to 13.2% in word error rate were achieved.

WordCluster: detecting clusters of DNA words and genomic elements

BackgroundMany k-mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds.ResultsWe introduce here an algorithm to detect clusters of DNA words (k-mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used WordCluster to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome.ConclusionsWordCluster seems to predict biological meaningful clusters of DNA words (k-mers) and genomic entities. The implementation of the method into a web server is available at http://bioinfo2.ugr.es/wordCluster/wordCluster.php including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes.

A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information

Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. Thus, we have developed an efficient and effective motif discovery method for ChIP experiments.

Data mining of mental health issues of non-bone marrow donor siblings

BackgroundAllogenic hematopoietic stem cell transplantation is a curative treatment for patients with advanced hematologic malignancies. However, the long-term mental health issues of siblings who were not selected as donors (non-donor siblings, NDS) in the transplantation have not been well assessed. Data mining is useful in discovering new findings from a large, multidisciplinary data set and the Scenario Map analysis is a novel approach which allows extracting keywords linking different conditions/events from text data of interviews even when the keywords appeared infrequently. The aim of this study is to assess mental health issues on NDSs and to find helpful keywords for the clinical follow-up using a Scenario Map analysis.FindingsA 47-year-old woman whose younger sister had undergone allogenic hematopoietic stem cell transplantation 20 years earlier was interviewed as a NDS. The text data from the interview transcriptions was analyzed using Scenario Mapping. Four clusters of words and six keywords were identified. Upon review of the word clusters and keywords, both the subject and researchers noticed that the subject has had mental health issues since the disease onset to date with being a NDS. The issues have been alleviated by her family.ConclusionsThis single subject study suggested the advantages of data mining in clinical follow-up for mental health issues of patients and/or their families.

Learning to Generate a Table-of-Contents with Supportive Knowledge

In the text summarization field, a table-of-contents is a type of indicative summary that is especially suited for locating information in a long document, or a set of documents. It is also a useful summary for a reader to quickly get an overview of the entire contents. The current models for generating a table-of-contents produced relatively low quality output with many meaningless titles, or titles that have no overlapping meaning with the corresponding contents. This problem may be due to the lack of semantic information and topic information in those models. In this research, we propose to integrate supportive knowledge into the learning models to improve the quality of titles in a generated table-of-contents. The supportive knowledge is derived from a hierarchical clustering of words, which is built from a large collection of raw text, and a topic model, which is directly estimated from the training data. The relatively good results of the experiments showed that the semantic and topic information supplied by supportive knowledge have good effects on title generation, and therefore, they help to improve the quality of the generated table-of-contents.

テキストからの学習におけるワーキングメモリの効果

This study examined the effect of working memory on learning from texts. In Experiment 1, participants preformed a word clustering task involving key words from an explanatory text (pretest), and then read the text, which was presented sentence-by-sentence. Next, they performed a second clustering task (post-test), a problem solving task, and a reading span test (RST). The results suggested that the individual differences of the RST scores correlated with the scores for problem solving. In Experiment 2, the results suggested that the individual differences of the RST scores influenced the clustering performance at the level of the situation model when the text was presented all together. Moreover, the result of multiple dimension scaling suggested that the situation models of high-span readers reflected the structure of the text more than those of low-span readers. These results indicate that readers with high reading span scores construct coherent situation models of texts and make use of them for learning from texts.

Rich entity recognition in English text

Entity type recognition is used as a pre-processing step in common applications like summarization of text, classifying documents or automatic answering of questions posed in natural language. Here, ‘entity’ refers to concrete and abstract objects identified by proper and common nouns. Entity recognition focuses on detecting instances of types like person, location, organization, and so on. For example, an entity recognizer would take as input: George Washington was the first President of the United States of America. and output: <noun.person> George Washington </noun.person> was the first <noun.person> President </noun.person> of the <noun.location> United States of America </noun.Location>. The task can be performed using machine learning techniques to train a system that recognizes entities with performance comparable to a human annotator. Challenges like the lack of a large annotated training data corpus, impossible nature of listing all entity types, and ambiguity in language make this problem hard. There are existing entity recognizers which perform this task but with fair performance. One of the ways adopted to improve the performance of an existing entity recognizer is feature engineering. We initially find out which of the existing features, used in the recognizer, affect the performance most strongly. We accomplish this by adding and removing one or more features at a time from the feature list. We then use the training data to train a model and test to find out which set of features are important. The evaluation metric involves finding the precision, recall and f-score (which is the harmonic mean of precision and recall). As a next step, we add new features like word clusters and bigram word features to find out any improvements. Word clusters help when the training data does not have some words, but words belonging to the same cluster are present in the training data. This helps tagging unseen words in the test set. We also experiment with varying the size of the training data to find out how it affects the performance. Additionally, we look into Wikipedia as a source of additional features for the training data. Wikipedia has an elaborate internal link structure that can provide vital information about the category of a word. This category can be linked to a broader-sensed entity type.

Balancing and clustering of words in the Burrows–Wheeler transform

Compression algorithms based on Burrows–Wheeler transform (BWT) take advantage of the fact that the word output of BWT shows a local similarity and then turns out to be highly compressible. The aim of the present paper is to study such “clustering effect” by using notions and methods from Combinatorics on Words. The notion of balance of a word plays a central role in our investigation. Empirical observations suggest that balance is actually the combinatorial property of input word that ensure optimal BWT compression. Moreover, it is reasonable to assume that the more balanced the input word is, the more local similarity we have after BWT (and therefore the better the compression is). This hypothesis is here corroborated by experiments on “real” text, by using local entropy as a measure of the degree of balance of a word. In the setting of Combinatorics on Words, a sound confirmation of previous hypothesis is given by a result of Mantaci et al. (2003) [27], which states that, in the case of a binary alphabet, there is an equivalence between circularly balanced words, words having a clusterized BWT, and the conjugates of standard words. In the case of alphabets of size greater than two, there is no more equivalence. The last section of the present paper is devoted to investigate the relationships between these notions, and other related ones (as, for instance, palindromic richness) in the case of a general alphabet.

Exploiting associations between word clusters and document classes for cross‐domain text categorization†

AbstractCross‐domain text categorization targets on adapting the knowledge learnt from a labeled source domain to an unlabeled target domain, where the documents from the source and target domains are drawn from different distributions. However, in spite of the different distributions in raw‐word features, the associations between word clusters (conceptual features) and document classes may remain stable across different domains. In this paper, we exploit these unchanged associations as the bridge of knowledge transformation from the source domain to the target domain by the non‐negative matrix tri‐factorization. Specifically, we formulate a joint optimization framework of the two matrix tri‐factorizations for the source‐ and target‐domain data, respectively, in which the associations between word clusters and document classes are shared between them. Then, we give an iterative algorithm for this optimization and theoretically show its convergence. The comprehensive experiments show the effectiveness of this method. In particular, we show that the proposed method can deal with some difficult scenarios where baseline methods usually do not perform well. © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 100–114, 2011

Lexicalization Degree of Figurative Meaning Affects the Mental Organization of Body-Related Metaphorical Words in Chinese

The purpose of the present study was to examine how body-related Chinese metaphorical words are organizedwhen the degree of lexicalization is taken into consideration. Three experiments were conducted usingmultidimensional scaling (MDS) and a priming paradigm. The results in Exp. 1 and 2 indicated that thedispersion of metaphorical words was influenced by degree of lexicalization to produce a clear separationbetween a cluster of highly lexicalized metaphorical words (HM) and a cluster of body-part words (BW) withpartially lexicalized metaphorical words (LM) scattering between these two clusters. Semantic priming effectbased on a word’s literal meaning was then assessed in Exp. 3 by contrasting a picture-word match conditionwith a picture-word mismatch condition using these three types of words. Significant positive priming effect wasfound for the BW words but not the LM words, while a reversed inhibitory effect was found for the HM words.Briefly, with the direct evidence from Exp. 1 and 2 showing a unique dispersion in the Euclidean distance mapand the indirect evidence from Exp. 3 revealing the existence of literal meanings for the LM but not the HMwords, this study showed that metaphorical words are organized based on their degree of lexicalization.

Physician-Patient Relationship: Intervention Opportunities for Multiple Myeloma patients' Needs

AbstractAbstract 2564 Background:Multiple myeloma (MM) accounts for about 14% of all newly diagnosed haematological cancers, and it is estimated that its incidence will rise considerably due to an increasingly aging population in the Western world. Treatment of myeloma has changed in the past decade leading to a possibility of reaching a complete remission (CR) of nearly 50%, a median survival of 5 years, and a 10-year survival rate of 20%. Most of the recent treatment strategies do not require hospitalization so that many patients spend a considerable amount of time at home managing all the difficulties related to the therapies often with little support. Even though this physically disabling and deteriorating condition has a great impact on patients, little is known about their (unmet) needs and quality of life (QOL). Aims and Methods:The purpose of this qualitative study was to identify dominant themes, links between domains of QoL from the patients' perspective and how these domains influence their expectations regarding QoL in patients with multiple myeloma, trying to identify possible interventional instruments. The sample consisted of 34 patients treated in 9 Italian Centres: 16 women and 18 men; median age 65 years (range 35–77); with a first diagnosis of MM between 1982 and 2009 (median time since diagnosis 3 years). From July 2009 to Nov 2009, 34 indepth interviews (60 minutes each) were conducted by clinical psychologists. Data from individual interviews were audiotaped and transcribed and then analysed with the “Text Emotional Analysis”, a methodology based on clinical-psychological models and on statistical multivariate analysis techniques. Results:The analysis, conducted with the statistical software Alceste (Analyse des Léxèmes Cooccurrents dans les Enoncés Simples d'un Texte), highlighted 4 word clusters (Table 1: the words that characterize the clusters are reported in the table with a decreasing χ2 value that indicates the importance of the single words in the belonging clusters): Cluster 1 “The relationship with family and friends”; Cluster 2 “The relationship with pain and drugs”; Cluster 3 “The elaboration of the experience”; Cluster 4 “The relationship with physicians and the hospital”.The position of the different clusters in the factorial space can also originate a second kind of analysis that is based on the relation between clusters (Table 2: values of the relationships between Clusters and Factors). On the first factor, cluster 4 and cluster 1 oppose themselves. This is the factor of relationships: the new relationships (the physician and the hospital) oppose to the old relationships (the family); the socialization with the new environment (positively connoted: attention, professionalism, commitment, ability) that is in opposition with the isolation and the shelter of the family (children, family, wife, mother, home). On the second factor, Cluster 2 and Cluster 1 oppose themselves. This is the factor that highlights the complexity of the experience towards the body due to the disease and the drugs (pain, effects, improvements, results, care, discomfort). The third factor is characterized by cluster 3 and expresses coping strategies towards ones condition (shame, companion, emotions, to get accustomed to). Conclusion:This study shows that one of the most important aspects for patients affected by MM is the creation/reorganization of all relationships. The patient's psychological dimension that defines the relationship with the haematologist is driven by an “all-absorbing” taking in charge of the physician fantasy, that often clashes with the hospital practicality. The relationship with other patients, with others that live the same experience, so called “companion” (from latin cum panis, who eats the same bread). Moving on from the results of this research, in the period of May 2010-July 2010, a first intervention was performed. In 5 pilot centres seminars were conducted by clinical psychologists in order to let patients interact with their physicians in a dedicated “space” different from a waiting room or an informative seminar. More than 100 people participated between patients, family members, physicians and nurses. With the findings of this research a questionnaire will be designed and administered to a wider multiple myeloma patient sample. [Display omitted] Disclosures:Pelagalli:Celgene: Research Funding. Boccadoro:Celgene, Janssen-Cilag: Consultancy, scientific advisory board, research support. Petrucci:Janssen-Cilag, Celgene: Honoraria.