The emotional recall task : juxtaposing recall and recognition-based affect scales

Existing affect scales typically involve recognition of emotions from a predetermined emotion checklist. However, a recognition-based checklist may fail to capture sufficient breadth and specificity of an individual’s recalled emotional experiences and may therefore miss emotions that frequently come to mind. More generally, how do recalled emotions differ from recognized emotions? To address these issues, we present and evaluate an affect scale based on recalled emotions. Participants are asked to produce 10 words that best described their emotions over the past month and then to rate each emotion for how often it was experienced. We show that average weighted valence of the words produced in this task, the Emotional Recall Task (ERT), is strongly correlated with scales related to general affect, such as the PANAS, Ryff’s Scales of Psychological Well-being, the Satisfaction with Life Scale, Depression Anxiety and Stress Scales, and a few other related scales. We further show that the Emotional Recall Task captures a breadth and specificity of emotions not available in other scales but that are nonetheless commonly reported as experienced emotions. We test a general version of the ERT (the ERT general) that is language neutral and can be used across cultures. Finally, we show that the ERT is valid in a test-retest paradigm. In sum, the ERT measures affect based on emotion terms relevant to an individual’s idiosyncratic experience. It is consistent with recognition-based scales, but also offers a new direction towards enriching our understanding of individual differences in recalled and recognized emotions

Discriminative Topic Mining via Category-Name Guided Text Embedding

Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsupervised way, which often generate topics that do not fit users' particular needs and yield suboptimal performance on downstream tasks. We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora. This new task not only helps a user understand clearly and distinctively the topics he/she is most interested in, but also benefits directly keyword-driven classification tasks. We develop CatE, a novel category-name guided text embedding method for discriminative topic mining, which effectively leverages minimal user guidance to learn a discriminative embedding space and discover category representative terms in an iterative manner. We conduct a comprehensive set of experiments to show that CatE mines high-quality set of topics guided by category names only, and benefits a variety of downstream applications including weakly-supervised classification and lexical entailment direction identification.Comment: WWW 2020. (Code: https://github.com/yumeng5/CatE

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

Empirical Methodology for Crowdsourcing Ground Truth

The process of gathering ground truth data through human annotation is a major bottleneck in the use of information extraction methods for populating the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to volume of data and lack of annotators. Typically these practices use inter-annotator agreement as a measure of quality. However, in many domains, such as event detection, there is ambiguity in the data, as well as a multitude of perspectives of the information examples. We present an empirically derived methodology for efficiently gathering of ground truth data in a diverse set of use cases covering a variety of domains and annotation tasks. Central to our approach is the use of CrowdTruth metrics that capture inter-annotator disagreement. We show that measuring disagreement is essential for acquiring a high quality ground truth. We achieve this by comparing the quality of the data aggregated with CrowdTruth metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical Relation Extraction, Twitter Event Identification, News Event Extraction and Sound Interpretation. We also show that an increased number of crowd workers leads to growth and stabilization in the quality of annotations, going against the usual practice of employing a small number of annotators.Comment: in publication at the Semantic Web Journa

Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which needs translation functionality of a relatively low level of sophistication since current models for information retrieval (IR) are still based on a bag-of-words. The Web provides a vast resource for the automatic construction of parallel corpora which can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this paper, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.Comment: 37 page