In psychology, attempts to replicate published findings are less successful than expected. For properly powered studies replication rate should be around 80%, whereas in practice less than 40% of the studies selected from different areas of psychology can be replicated. Researchers in cognitive psychology are hindered in estimating the power of their studies, because the designs they use present a sample of stimulus materials to a sample of participants, a situation not covered by most power formulas. To remedy the situation, we review the literature related to the topic and introduce recent software packages, which we apply to the data of two masked priming studies with high power. We checked how we could estimate the power of each study and how much they could be reduced to remain powerful enough. On the basis of this analysis, we recommend that a properly powered reaction time experiment with repeated measures has at least 1,600 word observations per condition (e.g., 40 participants, 40 stimuli). This is considerably more than current practice. We also show that researchers must include the number of observations in meta-analyses because the effect sizes currently reported depend on the number of stimuli presented to the participants. Our analyses can easily be applied to new datasets gathered.

/pdf/power-analysis-and-effect-size-in-mixed-effects-models-a-1ox1118bc1.pdf

Power Analysis and Effect Size in Mixed Effects Models: A Tutorial

Code-mixing is frequently observed in user generated content on social media, especially from multilingual users. The linguistic complexity of such content is compounded by presence of spelling variations, transliteration and non-adherance to formal grammar. We describe our initial efforts to create a multi-level annotated corpus of Hindi-English codemixed text collated from Facebook forums, and explore language identification, back-transliteration, normalization and POS tagging of this data. Our results show that language identification and transliteration for Hindi are two major challenges that impact POS tagging accuracy.

/pdf/pos-tagging-of-english-hindi-code-mixed-social-media-content-wzbx0d4taj.pdf

POS Tagging of English-Hindi Code-Mixed Social Media Content

교과서 문장의 Readability

Code-Mixing is a frequently observed phenomenon in social media content generated by multi-lingual users. The processing of such data for linguistic analysis as well as computational modelling is challenging due to the linguistic complexity resulting from the nature of the mixing as well as the presence of non-standard variations in spellings and grammar, and transliteration. Our analysis shows the extent of Code-Mixing in English-Hindi data. The classification of Code-Mixed words based on frequency and linguistic typology underline the fact that while there are easily identifiable cases of borrowing and mixing at the two ends, a large majority of the words form a continuum in the middle, emphasizing the need to handle these at different levels for automatic processing of the data.

/pdf/a-oei-am-borrowing-ya-mixing-a-an-analysis-of-english-hindi-w0ofjvv4wr.pdf

â€œI am borrowing ya mixing ?â€ An Analysis of English-Hindi Code Mixing in Facebook

Computers and computer-based technology have become an integral part of the lives of many individuals with disabilities. One of the most common activities that can be computer assisted is the generation of text. People who cannot accurately control their extremities (due to disabilities such as cerebral palsy and spinal cord injury) use computers as writing tools. People whose physical disability restricts their spoken output may use a computer as a communication prosthesis. In both cases, the generation of text is a necessary activity that can be physically demanding. It should be made as easy for the user as possible. While the standard computer keyboard is an efficient interface for able-bodied people and some disabled people, it may present significant access problems for others. In these cases some alternative interface is necessary.

Generating text from compressed input : an intelligent interface for people with severe motor impairments : Computer & people with disabilities

In this paper we have proposed a linguistically informed recursive neural network architecture for automatic extraction of cause-effect relations from text These relations can be expressed in arbitrarily complex ways The architecture uses word level embeddings and other linguistic features to detect causal events and their effects mentioned within a sentence The extracted events and their relations are used to build a causal-graph after clustering and appropriate generalization, which is then used for predictive purposes We have evaluated the performance of the proposed extraction model with respect to two baseline systems,one a rule-based classifier, and the other a conditional random field (CRF) based supervised model We have also compared our results with related work reported in the past by other authors on SEMEVAL data set, and found that the proposed bi-directional LSTM model enhanced with an additional linguistic layer performs better We have also worked extensively on creating new annotated datasets from publicly available data, which we are willing to share with the community

/pdf/automatic-extraction-of-causal-relations-from-text-using-2bmk1ots80.pdf

Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks

This paper presents a cross platform multilingual multimedia Indian Sign Language (ISL) dictionary building tool. ISL is a linguistically under-investigated language with no source of well documented electronic data. Research on ISL linguistics also gets hindered due to a lack of ISL knowledge and the unavailability of any educational tools. Our system can be used to associate signs corresponding to a given text. The current system also facilitates the phonological annotation of Indian signs in the form of HamNoSys structure. The generated HamNoSys string can be given as input to an avatar module to produce an animated sign representation.

A Multilingual Multimedia Indian Sign Language Dictionary Tool

In this paper we present a qualitatively enhanced deep convolution recurrent neural network for computing the quality of a text in an automatic essay scoring task. The novelty of the work lies in the fact that instead of considering only the word and sentence representation of a text, we try to augment the different complex linguistic, cognitive and psycological features associated within a text document along with a hierarchical convolution recurrent neural network framework. Our preliminary investigation shows that incorporation of such qualitative feature vectors along with standard word/sentence embeddings can give us better understanding about improving the overall evaluation of the input essays.

/pdf/augmenting-textual-qualitative-features-in-deep-convolution-5l3zj9slfc.pdf

Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring

This paper presents a prototype Text-To-Indian Sign Language (ISL) translation system. The system will help dissemination of information to the deaf people in India. This paper also presents the SL-dictionary tool, which can be used to create bilingual ISL dictionary and can store ISL phonological information.

Prototype Machine Translation System From Text-To-Indian Sign Language

http://www.computing.dcu.ie/~sdandapat/publication/conf_paper/nlplpl08.pdf

Tirthankar Dasgupta

Papers

Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks

A Multilingual Multimedia Indian Sign Language Dictionary Tool

Augmenting Textual Qualitative Features in Deep Convolution Recurrent Neural Network for Automatic Essay Scoring

Prototype Machine Translation System From Text-To-Indian Sign Language

Prototype machine translation system from text-to-Indian sign language