Matrix Factorization Techniques for Recommender Systems

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Language and the Internet

Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalising ill-formed words. Our method uses a classifier to detect ill-formed words, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. The proposed method doesn't require any annotations, and achieves state-of-the-art performance over an SMS corpus and a novel dataset based on Twitter.

/pdf/lexical-normalisation-of-short-text-messages-makn-sens-a-1rcrmye84i.pdf

Lexical Normalisation of Short Text Messages: Makn Sens a #twitter

Explaining language change: an evolutionary approach

Language usage over computer mediated discourses, such as chats, emails and SMS texts, significantly differs from the standard form of the language and is referred to as texting language (TL). The presence of intentional misspellings significantly decrease the accuracy of existing spell checking techniques for TL words. In this work, we formally investigate the nature and type of compressions used in SMS texts, and develop a Hidden Markov Model based word-model for TL. The model parameters have been estimated through standard machine learning techniques from a word-aligned SMS and standard English parallel corpus. The accuracy of the model in correcting TL words is 57.7%, which is almost a threefold improvement over the performance of Aspell. The use of simple bigram language model results in a 35% reduction of the relative word level error rates.

/pdf/investigation-and-modeling-of-the-structure-of-texting-33ah4ecwiy.pdf

Investigation and modeling of the structure of texting language

We report results of stylometric differences in blogging for gender and age group variation. The results are based on two mutually independent features. The first feature is the use of slang words which is a new concept proposed by us for Stylometric study of bloggers. Slang is a non-dictionary word that has evolved with time due to its frequent and popular usage. For the second feature, we have analysed the variation in average length of sentences across various age groups and gender. These two features are then augmented with previous study results reported in literature for stylometric analysis of age and gender. The combined feature list enhances the accuracy by a remarkable extent in predicting age and gender. These experiments were done on a 20,000 blog corpus. Experimental results show that these features work well in detection of bloggers’ demography. However, gender determination is more accurate than age group detection over a data spread across all ages but the accuracy of age prediction increases if we sample data with remarkable age difference.

Stylometric Analysis of Bloggers’ Age and Gender

Feature selection techniques for maximum entropy based biomedical named entity recognition

This paper describes our work on building Part-of-Speech (POS) tagger for Bengali. We have use Hidden Markov Model (HMM) and Maximum Entropy (ME) based stochastic taggers. Bengali is a morphologically rich language and our taggers make use of morphological and contextual information of the words. Since only a small labeled training set is available (45,000 words), simple stochastic approach does not yield very good results. In this work, we have studied the effect of using a morphological analyzer to improve the performance of the tagger. We find that the use of morphology helps improve the accuracy of the tagger especially when less amount of tagged corpora are available.

http://www.computing.dcu.ie/~sdandapat/publication/conf_paper/acl07.pdf

Automatic Part-of-Speech Tagging for Bengali: An Approach for Morphologically Rich Languages in a Poor Resource Scenario

We describe our effort in developing a Named Entity Recognition (NER) system for Hindi using Maximum Entropy (MaxEnt) approach. We developed a NER annotated corpora for the purpose. We have tried to identify the most relevant features for Hindi NER task to enable us to develop an efficient NER from the limited corpora developed. Apart from the orthographic and collocation features, we have experimented on the efficiency of using gazetteer lists as features. We also worked on semi-automatic induction of context patterns and experimented with using these as features of the MaxEnt method. We have evaluated the performance of the system against a blind test set having 4 classes Person, Organization, Location and Date. Our system achieved a f-value of 81.52%.

Sudeshna Sarkar

Papers

Investigation and modeling of the structure of texting language

Stylometric Analysis of Bloggers’ Age and Gender

Feature selection techniques for maximum entropy based biomedical named entity recognition

Automatic Part-of-Speech Tagging for Bengali: An Approach for Morphologically Rich Languages in a Poor Resource Scenario

A Hybrid Feature Set based Maximum Entropy Hindi Named Entity Recognition