ARCOQ: Arabic Closest Opposite Questions Dataset

This paper presents a dataset for closest opposite questions in Arabic language. The dataset is the first of its kind for the Arabic language. It is beneficial for the assessment of systems on the aspect of antonymy detection. The structure is similar to that of the Graduate Record Examination (GRE) closest opposite questions dataset for the English language. The introduced dataset consists of 500 questions, each contains a query word for which the closest opposite needs to be determined from among a set of candidate words. Each question is also associated with the correct answer. We publish the dataset publicly in addition to providing standard splits of the dataset into development and test sets. Moreover, the paper provides a benchmark for the performance of different Arabic word embedding models on the introduced dataset

New Vector-Space Embeddings for Recommender Systems

In this work, we propose a novel recommender system model based on a technology commonly used in natural language processing called word vector embedding. In this technology, a word is represented by a vector that is embedded in an n-dimensional space. The distance between two vectors expresses the level of similarity/dissimilarity of their underlying words. Since item similarities and user similarities are the basis of designing a successful collaborative filtering, vector embedding seems to be a good candidate. As opposed to words, we propose a vector embedding approach for learning vectors for items and users. There have been very few recent applications of vector embeddings in recommender systems, but they have limitations in the type of formulations that are applicable. We propose a novel vector embedding that is versatile, in the sense that it is applicable for the prediction of ratings and for the recommendation of top items that are likely to appeal to users. It could also possibly take into account content-based features and demographic information. The approach is a simple relaxation algorithm that optimizes an objective function, defined based on target users’, items’ or joint user–item’s similarities in their respective vector spaces. The proposed approach is evaluated using real life datasets such as “MovieLens”, “ModCloth”, “Amazon: Magazine_Subscriptions” and “Online Retail”. The obtained results are compared with some of the leading benchmark methods, and they show a competitive performance

A Polarity Capturing Sphere for Word to Vector Representation

Embedding words from a dictionary as vectors in a space has become an active research field, due to its many uses in several natural language processing applications. Distances between the vectors should reflect the relatedness between the corresponding words. The problem with existing word embedding methods is that they often fail to distinguish between synonymous, antonymous, and unrelated word pairs. Meanwhile, polarity detection is crucial for applications such as sentiment analysis. In this work we propose an embedding approach that is designed to capture the polarity issue. The approach is based on embedding the word vectors into a sphere, whereby the dot product between any vectors represents the similarity. Vectors corresponding to synonymous words would be close to each other on the sphere, while a word and its antonym would lie at opposite poles of the sphere. The approach used to design the vectors is a simple relaxation algorithm. The proposed word embedding is successful in distinguishing between synonyms, antonyms, and unrelated word pairs. It achieves results that are better than those of some of the state-of-the-art techniques and competes well with the others

Challenges and Facilitating Factors in Sustaining Community-Based Participatory Research Partnerships: Lessons Learned from the Detroit, New York City and Seattle Urban Research Centers

In order to address the social, physical and economic determinants of urban health, researchers, public health practitioners, and community members have turned to more comprehensive and participatory approaches to research and interventions. One such approach, community-based participatory research (CBPR) in public health, has received considerable attention over the past decade, and numerous publications have described theoretical underpinnings, values, principles and practice. Issues related to the long-term sustainability of partnerships and activities have received limited attention. The purpose of this article is to examine the experiences and lessons learned from three Urban Research Centers (URCs) in Detroit, New York City, and Seattle, which were initially established in 1995 with core support from the Centers for Disease Control and Prevention (CDC). The experience of these Centers after core funding ceased in 2003 provides a case study to identify the challenges and facilitating factors for sustaining partnerships. We examine three broad dimensions of CBPR partnerships that we consider important for sustainability: (1) sustaining relationships and commitments among the partners involved; (2) sustaining the knowledge, capacity and values generated from the partnership; and (3) sustaining funding, staff, programs, policy changes and the partnership itself. We discuss the challenges faced by the URCs in sustaining these dimensions and the strategies used to overcome these challenges. Based on these experiences, we offer recommendations for: strategies that partnerships may find useful in sustaining their CBPR efforts; ways in which a Center mechanism can be useful for promoting sustainability; and considerations for funders of CBPR to increase sustainability