We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.

Efficient Estimation of Word Representations in Vector Space

2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

Erik Eriksen is a remarkable individual. He has no college degrees yet is Professor of Human Development at Harvard University. He came to psychology via art, which explains why the reader will find him painting contexts and backgrounds rather than stating dull facts and concepts. He has been a training psychoanalyst for many years as well as a perceptive observer of cultural and social settings and their effect on growing up. This is not just a book on childhood. It is a panorama of our society. Anxiety in young children, apathy in American Indians, confusion in veterans of war, and arrogance in young Nazis are scrutinized under the psychoanalytic magnifying glass. The material is well written and devoid of technical jargon. The theme of the volume is that it is human to have a long childhood which will leave a lifelong residue of emotional immaturity in man. Primitive groups and

Childhood and Society.

Continuous space language models have recently demonstrated outstanding results across a variety of tasks. In this paper, we examine the vector-space word representations that are implicitly learned by the input-layer weights. We find that these representations are surprisingly good at capturing syntactic and semantic regularities in language, and that each relationship is characterized by a relation-specific vector offset. This allows vector-oriented reasoning based on the offsets between words. For example, the male/female relationship is automatically learned, and with the induced vector representations, “King Man + Woman” results in a vector very close to “Queen.” We demonstrate that the word vectors capture syntactic regularities by means of syntactic analogy questions (provided with this paper), and are able to correctly answer almost 40% of the questions. We demonstrate that the word vectors capture semantic regularities by using the vector offset method to answer SemEval-2012 Task 2 questions. Remarkably, this method outperforms the best previous systems.

/pdf/linguistic-regularities-in-continuous-space-word-1d1g189hxf.pdf

Linguistic Regularities in Continuous Space Word Representations

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Social networks are often grounded in spatial locality where individuals form relationships with those they meet nearby. However, the location of individuals in online social networking platforms is often unknown. Prior approaches have tried to infer individuals' locations from the content they produce online or their online relations, but often are limited by the available location-related data. We propose a new method for social networks that accurately infers locations for nearly all of individuals by spatially propagating location assignments through the social network, using only a small number of initial locations. In five experiments, we demonstrate the effectiveness in multiple social networking platforms, using both precise and noisy data to start the inference, and present heuristics for improving performance. In one experiment, we demonstrate the ability to infer the locations of a group of users who generate over 74% of the daily Twitter message volume with an estimated median location error of 10km. Our results open the possibility of gathering large quantities of location-annotated data from social media platforms.

/pdf/that-s-what-friends-are-for-inferring-location-in-online-7yi72qds47.pdf

That's What Friends Are For: Inferring Location in Online Social Media Platforms Based on Social Relationships

This paper presents the SemEval-2013 task on multilingual Word Sense Disambiguation. We describe our experience in producing a multilingual sense-annotated corpus for the task. The corpus is tagged with BabelNet 1.1.1, a freely-available multilingual encyclopedic dictionary and, as a byproduct, WordNet 3.0 and the Wikipedia sense inventory. We present and analyze the results of participating systems, and discuss future directions.

/pdf/semeval-2013-task-12-multilingual-word-sense-disambiguation-4wgfa7j5h8.pdf

SemEval-2013 Task 12: Multilingual Word Sense Disambiguation

Using footage from body-worn cameras, we analyze the respectfulness of police officer language toward white and black community members during routine traffic stops. We develop computational linguistic methods that extract levels of respect automatically from transcripts, informed by a thin-slicing study of participant ratings of officer utterances. We find that officers speak with consistently less respect toward black versus white community members, even after controlling for the race of the officer, the severity of the infraction, the location of the stop, and the outcome of the stop. Such disparities in common, everyday interactions between police and the communities they serve have important implications for procedural justice and the building of police-community trust.

https://www.pnas.org/content/pnas/114/25/6521.full.pdf

Language from police body camera footage shows racial disparities in officer respect.

Up to now, work on semantic relations has focused on relation classification: recognizing whether a given instance (a word pair such as virus:flu) belongs to a specific relation class (such as CAUSE:EFFECT). However, instances of a single relation class may still have significant variability in how characteristic they are of that class. We present a new SemEval task based on identifying the degree of prototypicality for instances within a given class. As a part of the task, we have assembled the first dataset of graded relational similarity ratings across 79 relation categories. Three teams submitted six systems, which were evaluated using two methods.

/pdf/semeval-2012-task-2-measuring-degrees-of-relational-8l529uzssj.pdf

SemEval-2012 Task 2: Measuring Degrees of Relational Similarity

Geolocated social media data provides a powerful source of information about place and regional human behavior.  Because little social media data is geolocation-annotated, inference techniques serve an essential role for increasing the volume of annotated data. One major class of inference approaches has relied on the social network of Twitter, where the locations of a user's friends serve as evidence for that user's location. While many such inference techniques have been recently proposed, we actually know little about their relative performance, with the amount of ground truth data varying between 5% and 100% of the network, the size of the social network varying by four orders of magnitude, and little standardization in evaluation metrics. We conduct a systematic comparative analysis of nine state-of-the-art network-based methods for performing geolocation inference at the global scale, controlling for the source of ground truth data, dataset size, and temporal recency in test data. Furthermore, we identify a comprehensive set of evaluation metrics that clarify performance differences. Our analysis identifies a large performance disparity between that reported in the literature and that seen in real-world conditions. To aid reproducibility and future comparison, all implementations have been released in an open source geoinference package.

/pdf/geolocation-prediction-in-twitter-using-social-networks-a-j9h6gv73vt.pdf

David Jurgens

Papers

That's What Friends Are For: Inferring Location in Online Social Media Platforms Based on Social Relationships

SemEval-2013 Task 12: Multilingual Word Sense Disambiguation

Language from police body camera footage shows racial disparities in officer respect.

SemEval-2012 Task 2: Measuring Degrees of Relational Similarity

Geolocation Prediction in Twitter Using Social Networks: A Critical Analysis and Review of Current Practice