Self-training on refined clause patterns for relation extraction

Within the context of Information Extraction (IE), relation extraction is oriented towards identifying a variety of relation phrases and their arguments in arbitrary sentences. In this paper, we present a clause-based framework for information extraction in textual documents. Our framework focuses on two important challenges in information extraction: 1) Open Information Extraction and (OIE), and 2) Relation Extraction (RE). In the plethora of research that focus on the use of syntactic and dependency parsing for the purposes of detecting relations, there has been increasing evidence of incoherent and uninformative extractions. The extracted relations may even be erroneous at times and fail to provide a meaningful interpretation. In our work, we use the English clause structure and clause types in an effort to generate propositions that can be deemed as extractable relations. Moreover, we propose refinements to the grammatical structure of syntactic and dependency parsing that help reduce the number of incoherent and uninformative extractions from clauses. In our experiments both in the open information extraction and relation extraction domains, we carefully evaluate our system on various benchmark datasets and compare the performance of our work against existing state-of-the-art information extraction systems. Our work shows improved performance compared to the state-of-the-art techniques.     

Neural embedding-based indices for semantic search

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.     

The complexity of trauma response: a 4-year follow-up of adolescent Cambodian refugees

OBJECTIVE: The objective of this study was to document the psychosocial adjustment of young refugees during their adolescence and its association with the war-related trauma experienced by their family before migration. METHOD: Data were collected on 57 young Khmer resettled in Montreal and followed from early to late adolescence. The associations between premigratory exposure to political violence and postmigratory mental health and social adjustment were estimated for early, mid-, and late adolescence. RESULTS: The associations between premigratory exposure to political violence and postmigratory psychosocial adjustment fluctuated over the adolescence period. Overall, the adolescents whose families were more highly exposed to political violence tended to report a more positive social adjustment and less mental health symptoms than those less exposed. CONCLUSION: The high expectations of Cambodian parents towards their children and the preservation of traditional values despite the Khmer rouge attempts to eradicate them might contribute to explain the paradoxical association between the families' exposure to political violence and the adolescents' psychosocial adjustment in the host country. Although children and adult refugees seen in clinical setting are reminders of the negative effects of adversity, resilience should be more systematically explored in community samples to further our understanding of the long-term effects of trauma.     

Developing a mathematics curriculum to improve learning behaviors and mathematics competency of children

The authors investigated the effect of a mathematical curriculum (CU) developed based on verbal and practical activities on the mathematical competency (MC) and learning behaviors (LB) of preschool children. In a quasi-experimental design, 60 children (5- to 6-year-old girls) were selected using the accessible sampling method. The children were randomly divided into an experimental group and a control group, and the relevant concepts were taught to the children in both groups. While the control group received the typical kindergarten education based on the usual textbooks and worksheets, the CU was taught to the experimental group. Structural equation modeling was used to model the data and statistical evaluation. The results demonstrated a significant difference between the two groups in MC and LB. The CU significantly improved MC directly, and indirectly through the improvement of LB (i.e., engagement and learning focus, verbal behaviors, and type of activity).     

Problematic placement: pathways pre-service teachers’ perspectives on their infant/toddler placement

The inclusion of an infant/toddler placement in a ‘pathways’ early childhood teaching degree, where students already have qualifications and experience in working with young children, can be problematic. This pilot study investigated student teachers’ views on their infant/toddler (birth-to-two-years) placement. Sixty-six students completing their early childhood education degree at an Australian university responded to a survey seeking their perspectives on the effectiveness of the placement in developing teacher confidence, knowledge and skills, and the quality of the supervision they received. The participants had entered their degree with a two-year Diploma of Children’s Services. Responses indicated significant dissatisfaction with the quality of supervision, the absence of teacher-mentors, and the lack of opportunities to practise new approaches. Participants commented that they ‘already knew’ how to work with this age group, and that they aspired to work with older children. The results align with other findings on factors associated with positive placements, and raise questions about the effectiveness of the infant/toddler placement in its current form.     

What Does She Expect When She Dresses Like That? Teacher Interpretation of Emerging Adolescent Female Sexuality

When examining the experiences of adolescent girls, a study into the presumptions teachers have regarding female adolescent sexuality is a very important aspect to explore. This article presents the findings from a study we conducted with eleven middle- and high school teachers in a southeastern state from both rural and urban districts. In-depth interviews were conducted to determine how their experiences and perceptions impact their understanding of the emerging sexuality of students in their classrooms. Several findings emerged, including that girls continue to be placed in contradictory positions concerning sexuality, that adverse sexual labels continue to serve as a means of sexual harassment that many teachers do not recognize, and that perceptions of sexuality and acceptable behavior remain deeply embedded in race and class issues.     

Comparing creative thinking abilities and reasoning ability of deaf and hearing children

This study focuses on comparing the creative thinking and reasoning abilities of deaf and hearing children. Two groups of deaf (N = 210) and hearing children (N = 200) were chosen based on specific criteria. Two instruments were used in the study: theTorrance Tests of Creative Thinking‐Figural, Form A and Matrix Analogous Test. Canonical Correlation analysis and multivariate analysis of variance (MANOVA) were used to analyze the data. The canonical correlation analyses revealed one significant dimension in both deaf and hearing children. The multivariate analysis of variance (MANOVA) revealed that there are some similarities and differences between the deaf and hearing samples regarding creative thinking abilities.     

Feature-enriched matrix factorization for relation extraction

Relation extraction aims at finding meaningful relationships between two named entities from within unstructured textual content. In this paper, we define the problem of information extraction as a matrix completion problem where we employ the notion of universal schemas formed as a collection of patterns derived from open information extraction systems as well as additional features derived from grammatical clause patterns and statistical topic models. One of the challenges with earlier work that employ matrix completion methods is that such approaches require a sufficient number of observed relation instances to be able to make predictions. However, in practice there is often insufficient number of explicit evidence supporting each relation type that could be used within the matrix model. Hence, existing work suffer from a low recall. In our work, we extend the work in the state of the art by proposing novel ways of integrating two sets of features, i.e., topic models and grammatical clause structures, for alleviating the low recall problem. More specifically, we propose that it is possible to (1) employ grammatical clause information from textual sentences to serve as an implicit indication of relation type and argument similarity. The basis for this is that it is likely that similar relation types and arguments are observed within similar grammatical structures, and (2) benefit from statistical topic models to determine similarity between relation types and arguments. We employ statistical topic models to determine relation type and argument similarity based on their co-occurrence within the same topics. We have performed extensive experiments based on both gold standard and silver standard datasets. The experiments show that our approach has been able to address the low recall problem in existing methods, by showing an improvement of 21% on recall and 8% on f-measure over the state of the art baseline.