Alien Registration- Dumais, Yvonne (Brunswick, Cumberland County)

https://digitalmaine.com/alien_docs/31716/thumbnail.jp

Entity linking of tweets based on dominant entity candidates

© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Entity linking, also known as semantic annotation, of textual content has received increasing attention. Recent works in this area have focused on entity linking on text with special characteristics such as search queries and tweets. The semantic annotation of tweets is specially proven to be challenging given the informal nature of the writing and the short length of the text. In this paper, we propose a method to perform entity linking on tweets built based on one primary hypothesis. We hypothesize that while there are formally many possible entity candidates for an ambiguous mention in a tweet, as listed on the disambiguation page of the corresponding entity on Wikipedia, there are only few entity candidates that are likely to be employed in the context of Twitter. Based on this hypothesis, we propose a method to identify such dominant entity candidates for each ambiguous mention and use them in the annotation process. Particularly, our proposed work integrates two phases (i) dominant entity candidate detection, which applies community detection methods for finding the dominant candidates of ambiguous mentions; and (ii) named entity disambiguation that links a tweet to entities in Wikipedia by only considering the identified dominant entity candidates. Our investigations show that: (1) there are only very few entity candidates for each ambiguous mention in a tweet that need to be considered when performing disambiguation. This helps us limit the candidate search space and hence noticeably reduce the entity linking time; (2) limiting the search space to only a subset of disambiguation options will not only improve entity linking execution time but will also lead to improved accuracy of the entity linking process when the main entity candidates of each mention are mined from a temporally aligned corpus. We show that our proposed method offers competitive results with the state-of-the-art methods in terms of precision and recall on widely used gold standard datasets while significantly reducing the time for processing each tweet

Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Defining "Development".

Is it possible, and in the first place is it even desirable, to define what "development" means and to determine the scope of the field called "developmental biology"? Though these questions appeared crucial for the founders of "developmental biology" in the 1950s, there seems to be no consensus today about the need to address them. Here, in a combined biological, philosophical, and historical approach, we ask whether it is possible and useful to define biological development, and, if such a definition is indeed possible and useful, which definition(s) can be considered as the most satisfactory

What is the fate of the river waters of Hudson Bay?

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 88 (2011): 352-361, doi:10.1016/j.jmarsys.2011.02.004.We examine the freshwater balance of Hudson and James bays, two shallow and fresh seas that annually receive 12% of the pan- Arctic river runoff. The analyses use the results from a 3–D sea ice-ocean coupled model with realistic forcing for tides, rivers, ocean boundaries, precipitation, and winds. The model simulations show that the annual freshwater balance is essentially between the river input and a large outflow toward the Labrador shelf. River waters are seasonally exchanged from the nearshore region to the interior of the basin, and the volumes exchanged are substantial (of the same order of magnitude as the annual river input). This lateral exchange is mostly caused by Ekman transport, and its magnitude and variability are controlled by the curl of the stress at the surface of the basin. The average transit time of the river waters is 3.0 years, meaning that the outflow is a complex mixture of the runoff from the three preceding years.We thank NSERC and the Canada Research Chairs program for funding. FS acknowledges support from NSF OCE-0751554 and ONR N00014-08-10490

Rocchio Algorithm to Enhance Semantically Collaborative Filtering

International audienceRecommender system provides relevant items to users from huge catalogue. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for recommendation. Hybrid recommendation system combines the two techniques. In this paper, we present another hybridization approach: User Semantic Collaborative Filtering. The aim of our approach is to predict users preferences for items based on their inferred preferences for semantic information of items. In this aim, we design a new user semantic model to describe the user preferences by using Rocchio algorithm. Due to the high dimension of item content, we apply a latent semantic analysis to reduce the dimension of data. User semantic model is then used in a user-based collaborative filtering to compute prediction ratings and to provide recommendations. Applying our approach to real data set, the MoviesLens 1M data set, significant improvement can be noticed compared to usage only approach, content based only approach

Using semantic clustering to support situation awareness on Twitter: The case of World Views

In recent years, situation awareness has been recognised as a critical part of effective decision making, in particular for crisis management. One way to extract value and allow for better situation awareness is to develop a system capable of analysing a dataset of multiple posts, and clustering consistent posts into different views or stories (or, world views). However, this can be challenging as it requires an understanding of the data, including determining what is consistent data, and what data corroborates other data. Attempting to address these problems, this article proposes Subject-Verb-Object Semantic Suffix Tree Clustering (SVOSSTC) and a system to support it, with a special focus on Twitter content. The novelty and value of SVOSSTC is its emphasis on utilising the Subject-Verb-Object (SVO) typology in order to construct semantically consistent world views, in which individuals---particularly those involved in crisis response---might achieve an enhanced picture of a situation from social media data. To evaluate our system and its ability to provide enhanced situation awareness, we tested it against existing approaches, including human data analysis, using a variety of real-world scenarios. The results indicated a noteworthy degree of evidence (e.g., in cluster granularity and meaningfulness) to affirm the suitability and rigour of our approach. Moreover, these results highlight this article's proposals as innovative and practical system contributions to the research field

An Osmotic Model of the Growing Pollen Tube

Pollen tube growth is central to the sexual reproduction of plants and is a longstanding model for cellular tip growth. For rapid tip growth, cell wall deposition and hardening must balance the rate of osmotic water uptake, and this involves the control of turgor pressure. Pressure contributes directly to both the driving force for water entry and tip expansion causing thinning of wall material. Understanding tip growth requires an analysis of the coordination of these processes and their regulation. Here we develop a quantitative physiological model which includes water entry by osmosis, the incorporation of cell wall material and the spreading of that material as a film at the tip. Parameters of the model have been determined from the literature and from measurements, by light, confocal and electron microscopy, together with results from experiments made on dye entry and plasmolysis in Lilium longiflorum. The model yields values of variables such as osmotic and turgor pressure, growth rates and wall thickness. The model and its predictive capacity were tested by comparing programmed simulations with experimental observations following perturbations of the growth medium. The model explains the role of turgor pressure and its observed constancy during oscillations; the stability of wall thickness under different conditions, without which the cell would burst; and some surprising properties such as the need for restricting osmotic permeability to a constant area near the tip, which was experimentally confirmed. To achieve both constancy of pressure and wall thickness under the range of conditions observed in steady-state growth the model reveals the need for a sensor that detects the driving potential for water entry and controls the deposition rate of wall material at the tip