For online social networks, demographic analysis is absolutely essential for improving their services in many ways. It is instrumental in understanding their different audiences, members and competitors. As well as that, it is pivotal in designing effective personalization and contextualization strategies, especially for displaying and creating better content. There is, for this reason, a great bulk of research into how demographic variables are characterized and how they impact online platforms such as Facebook and Twitter. But surprisingly, only a handful of works delve into their characterization and effect on community Question-Answering (cQA) websites. In this particular context, the subject of age demographics remains largely unexplored. This paper takes the lead on interpreting automatic age recognition on CQAs (a.k.a. age screening) as a regression task. To this effect, it compares state-of-the-art graph-based neural network regression and embedding models on a massive activity-graph encompassing ca. 16 and 837 million nodes (members) and edges, respectively. For this study, a large-scale subset of ca. 657,000 community fellows was automatically associated with their age via aligning their profile texts with a limited number of linguistic patterns. In short, our results show that Node2vec significantly outperforms other embeddings regardless of the regression model used for casting predictions. When this embedding is combined with Artificial Neural Network Regressions, we obtained our best configuration scoring a Root Mean Square Error (RMSE) of 8.39. An interesting qualitative feature of this embedding space is that age-based centroid vectors tend to form a trail ordered by age. Lastly, our outcomes also signal that activity graph based models can rival its counterparts based on image and textual inputs, paving the way for constructing effective multi-modal approaches.

This paper proposes network centrality as a criterion to designate a gateway or sink in real-time wireless sensor-actuator networks (WSAN). The objective is to improve network schedulability by design, particularly, by means of a centrality-driven gateway designation. To this purpose, four classical centrality metrics taken from social network analysis are explored, namely, (i) degree centrality, (ii) closeness centrality, (iii) betweenness centrality, and (iv) eigenvector centrality. We assume time-synchronized channel-hopping (TSCH) WSANs under centralized shortest-path routing and earliest-deadline-first (EDF) scheduling. Simulation results under varying configurations show that assigning the gateway role based on network centrality is, in general, an effective and promising approach to improve real-time performance in this type of networks. To the best of our knowledge, this work pioneers the use of a centrality-driven gateway designation as a mean to improve schedulability in WSANs.

We present a novel method for ranking query paraphrases for effective search in community question answering (cQA). The method uses query logs from Yahoo! Search and Yahoo! Answers for automatically extracting a corpus of paraphrases of queries and questions using the query-question click history. Elements of this corpus are automatically ranked according to recall and mean reciprocal rank, and then used for learning two independent learning to rank models (SVMRank), whereby a set of new query paraphrases can be scored according to recall and MRR. We perform several automatic evaluation procedures using cross-validation for analyzing the behavior of various aspects of our learned ranking functions, which show that our method is useful and effective for search in cQA.

Question–answering systems make good use of knowledge bases (KBs, e.g.,Wikipedia) for responding to definition queries. Typically, systems extract relevant facts from articles regarding the question across KBs, and then they are projected into the candidate answers. However, studies have shown that the performance of this kind of method suddenly drops, whenever KBs supply narrow coverage. This work describes a new approach to deal with this problem by constructingcontext modelsfor scoring candidate answers, which are, more precisely, statisticaln‐gram language models inferred fromlexicalized dependency pathsextracted fromWikipediaabstracts. Unlike state‐of‐the‐art approaches,context modelsare created by capturing the semantics of candidate answers (e.g., “novel,”“singer,”“coach,” and “city”). This work is extended by investigating the impact oncontext modelsof extra linguistic knowledge such as part‐of‐speech tagging and named‐entity recognition. Results showed the effectiveness ofcontext modelsasn‐gram lexicalized dependency paths and promising context indicators for the presence of definitions in natural language texts.

In the context of question-answering systems, there are several strategies for scoring candidate answers to definition queries including centroid vectors, bi-term and context language models. These techniques use only positive examples (i.e., descriptions) when building their models. In this work, a maximum entropy based extension is proposed for context language models so as to account for regularities across non-descriptions mined from web-snippets. Experiments show that this extension outperforms other strategies increasing the precision of the top five ranked answers by more than 5%. Results suggest that web-snippets are a cost-efficient source of non-descriptions, and that some relationships extracted from dependency trees are effective to mine for candidate answer sentences.

We present an evolutionary approach for the computation of exact answers to natural languages (NL) questions. Answers are extracted directly from the N-best snippets, which have been identified by a standard Web search engine using NL questions. The core idea of our evolutionary approach to Web question answering is to search for those substrings in the snippets whose contexts are most similar to contexts of already known answers. This context model together with the words mentioned in the NL question are used to evaluate the fitness of answer candidates, which are actually randomly selected substrings from randomly selected sentences of the snippets. New answer candidates are then created by applying specialized operators for crossover and mutation, which either stretch and shrink the substring of an answer candidate or transpose the span to new sentences. Since we have no predefined notion of patterns, our context alignment methods are very dynamic and strictly data-driven. We assessed our system with seven different datasets of question/answer pairs. The results show that this approach is promising, especially when it deals with specific questions.