Review of the following publication: Alex Housen, Folkert Kuiken & Ineke Vedder Dimensions of L2 Performance and Proficiency - Complexity, Accuracy and Fluency in SLA. Amsterdam/Philadelphia: John Benjamnins Publishing. Published on linguistlist April 2013.

Review of the book 'Dimensions of L2 performance and proficiency - Complexity, Accuracy and Fluency in SLA'

Automated Evaluation of Text and Discourse with Coh-Metrix: The Introduction

This manuscript presents a preliminary discussion of Artificial Intelligence (AI) as a disruptive technology with the potential to significantly change special education practices. The article begins with a brief description of the development of AI. The authors recognize our assertions are subjective and require further research. Several references are not peer-reviewed because educational research takes years to conduct, analyze, and disseminate outcomes. In this manuscript, we discuss current software used for writing with students in special education and discuss similarities and differences with AI software. This discussion is followed by questions and examples related to implementation, ethical and policy considerations, and preservice special education teacher preparation. The article concludes with future considerations for how AI will impact the special education technology field.

The Future of Artificial Intelligence in Special Education Technology

Sentiment analysis aims to identify the way in which sentiments are expressed in texts. State-of-the-art approaches base their analyses solely on the given text, which complicates the detection of implicit sentiments and ignores the role of sentiment contagion. In the financial domain, sentiment could be spread across multiple platforms, such as in company and analyst reports, news articles, and microblogs. Thus, to capture implicit sentiments and the contagion process, we introduce a novel approach that leverages the text and contextual information of a record for fine-grained sentiment analysis. Based on this information, we generate a record representation, which is used in an adapted feed-forward neural network. Our proposed solution improves the performance by as much as 15% and 234% relative to multiple baselines. Our work demonstrates the impact of implicit sentiment as well as the importance of different relationships for sentiment prediction on company and analyst reports, news articles, and microblogs. For example, we identified timestamp information as being non-essential for the fine-grained sentiment analysis of company and analyst reports. Although we are able to showcase improvements in financial sentiment analysis, sentiment contagion and limited context are two common problems that continue to prevail. Therefore, by re-defining sentiment analysis as a multi-text problem, our proposed solution can be applied across multiple domains and text types, such as product reviews.

Exploiting textual and relationship information for fine-grained financial sentiment analysis

Predicting CEFR levels in learners of English: The use of microsystem criterial features in a machine learning approach

The objective of this paper is to report on the building of a Sentiment Analysis (SA) system dedicated to financial microblogs in English. The purpose of our work is to build a financial classifier that predicts the sentiment of stock investors in microblog platforms such as StockTwits
and Twitter. Our contribution shows that it is possible to conduct such tasks in order to provide finegrained SA of financial microblogs. We extracted financial entities with relevant contexts and assigned scores on a continuous scale by adopting a deep learning method for the classification.
Results show a 0.85 F1Score on a twoclass basis and a 0.62 cosine similarity score.

https://hal.univ-rennes2.fr/hal-02280169/document

FinSentiA: sentiment analysis in English financial microblogs

This paper describes the workflow and architecture adopted by a linguistic research project on learner data. We report our experienceand present the research outputs turned into resources that we wish to share with the community. We discuss the current limitations andthe next steps that could be taken for the scaling and development of our research project. Allying NLP and language-centric AI, wediscuss similar projects and possible ways to start collaborating towards potential platform interoperability.

https://hal.archives-ouvertes.fr/hal-02634745/document

From Linguistic Research Projects to Language Technology Platforms: A Case Study in Learner Data

This paper describes the workflow and architecture adopted by a linguistic research project. We report our experience and present the research outputs turned into resources that we wish to share with the community. We discuss the current limitations and the next steps that could be taken for the scaling and development of our research project. Allying NLP and language-centric AI, we discuss similar projects and possible ways to start collaborating towards potential platform interoperability.

From Linguistic Research Projects to Language Technology Platforms: A Case Study in Learner Data.

Automatic Emotion Detection (ED) aims to build systems to identify users’ emotions automatically. This field has the potential to enhance HCI, creating an individualised experience for the user. However, ED systems tend to perform poorly on people with Autism Spectrum Disorder (ASD). Hence, the need to create ED systems tailored to how people with autism express emotions. Previous works have created ED systems tailored for children with ASD but did not share the resulting dataset. Sharing annotated datasets is essential to enable the development of more advanced computer models for ED within the research community. In this paper, we describe our experience establishing a process to create a multimodal annotated dataset featuring children with a level 1 diagnosis of autism. In addition, we introduce CALMED (Children, Autism, Multimodal, Emotion, Detection), the resulting multimodal emotion detection dataset featuring children with autism aged 8–12. CALMED includes audio and video features extracted from recording files of study sessions with participants, together with annotations provided by their parents into four target classes. The generated dataset includes a total of 57,012 examples, with each example representing a time window of 200 ms (0.2 s). Our experience and methods described here, together with the dataset shared, aim to contribute to future research applications of affective computing in ASD, which has the potential to create systems to improve the lives of people with ASD. 

Introducing CALMED: Multimodal Annotated Dataset for Emotion Detection in Children with Autism

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