Abstract

n this paper, we present a generic semi-supervised Word Sense Disambiguation(WSD) method. Currently, the existingWSD methods extensively use domain re-sources and linguistic knowledge.Ourproposed method extractscontext basedlistsfrom a small sense-tagged and un-tagged training data without using do-main knowledge. Experiments on Hindiand Marathi Tourism and Health domainsshow that it gives good performance with-out using any language specific linguisticinformation except the sense IDs presentin the sense-tagged training set and workswell even with small training data by han-dling the data sparsity issue. Other ad-vantages are that domain expertise is notneeded for crafting and selecting featuresto build the WSD model and it can handlethe problem of non availability of match-ing contexts in sense-tagged training set.It also finds sense IDs of those test wordswhich are not present in sense-tag

Abstract

Word embeddings learned from text corpus can be improved by injecting knowledge from external resources, while at the same time also specializing them for similarity or relatedness. These knowledge resources (like WordNet, Paraphrase Database) may not exist for all languages. In this work we introduce a method to inject word embeddings of a language with knowledge resource of another language by leveraging bilingual embeddings. First we improve word embeddings of German, Italian, French and Spanish using resources of English and test them on variety of word similarity tasks. Then we demonstrate the utility of our method by creating improved embeddings for Urdu and Telugu languages using Hindi WordNet, beating the previously established baseline for Urdu.

Abstract

We propose an unsupervised approach to extract all competing algorithms present in a given scholarly article. The algorithm names are treated as named entities and natural language processing techniques are used to extract them.All extracted entity names are linked with their respective original papers in the reference section by our novel entity-citation linking algorithm. Then the seen citation pairs are ranked based on the number of comparison related cue-words present in the entity-citation context. We manually annotated a small subset of DBLP Computer Science conference papers and report both qualitative and quantitative results of our algorithm on it

Abstract

With the advent of statistical modeling in sports, predicting the outcome of a game has been established as a fundamental problem.Cricket is one of the most popular team games in the world. With this article, we embark on predicting the outcome of a One Day International (ODI) cricket match using a supervised learning approach from a team composition perspective. Our work suggests that the relative team strength between the competing teams forms a distinctive feature for predicting the winner. Modeling the team strength boils down to modeling individual player’s batting and bowling performances, forming the basis of our approach. We use career statistics as well as the recent performances of a player to model him. Player independent factors have also been considered in order to predict the outcome of a match. We show that the k-Nearest Neighbor (kNN) algorithm yields better results as compared to other classifiers

Abstract

In this paper we investigate the problem of user authentication based on keystroke timing pattern. We propose a simple, robust and non parameterized nearest neighbor regression based feature ranking algorithm for anomaly detection. Our approach successfully handle drawbacks like outlier detection, scale variation and prevents over fitting.Apart from using existing keystroke timing features from the dataset liked well time and flight time, other features namely big ram time and inversion ratio time are engineered as well. The efficiency and effectiveness ofour method is demonstrated through extensive comparisons with others tate-of-the-art techniques using CMU keystroke dynamics bench markage equal error rate(EER) than other proposed techniques. We achieved an average equal error rate of0.051for the user authentication task.

Abstract

We present here a data mining approach for part-of-speech (POS) tagging, an important natural language processing (NLP) task, which is a classification problem. We propose a semi-supervised associative classification method for POS tagging. Existing methods for building POS taggers require extensive domain and linguistic knowledge and resources. Our method uses a combination of a small POS tagged corpus and untagged text data as training data to build the classifier model using association rules. Our tagger works well with very little training data also. The use of semi-supervised learning provides the advantage of not requiring a large high-quality annotated corpus. These properties make it especially suitable for resource-poor languages. Our experiments on various resource-rich, resource-moderate and resource-poor languages show good performance without using any language-specific linguistic information. We note that inclusion of such features in our method may further improve the performance. Results also show that for smaller training data sizes our tagger performs better than state-of-the-art conditional random field (CRF) tagger using same features as our tagger.

Abstract

In this paper, we consider the problem of learning representations for authors from bibliographic co-authorship networks. Existing methods for deep learning on graphs, such as DeepWalk, suffer from link sparsity problem as they focus on modeling the link information only. We hypothesize that capturing both the content and link information in a unified way will help mitigate the sparsity problem. To this end, we present a novel model'Author2Vec', which learns low-dimensional author representations such that authors who write similar content and share similar network structure are closer in vector space. Such embeddings are useful in a variety of applications such as link prediction, node classification, recommendation and visualization. The author embeddings we learn are empirically shown to outperform DeepWalk by 2.35% and 0.83% for link prediction and clustering task respectively.

Abstract

Plug loads accounts for 20% to 30% of building energy consumption and has an increasing trend. Automatic plug load identification is one of the technique for effectively managing the plug load consumption.There are several studies on Non Intrusive load monitoring (NILM) but limited studies on Intrusive load monitoring (ILM). ILM is a technique that uses a low end power meter on every plug load to monitor it’s power consumption. In this paper, machine-l earning techniques are applied on low-frequency ILM data for identification of plug load and it’s state (ON/Sleep).The results show the identification accuracies are close to 98%

Abstract

Measuring “similarity” has been established as a fundamental problem and has been widely studied. In this paper we propose a novel approach for establishing similarity in context of citation network. With the rapidly growing size of academic literature, the problem of finding similar research papers has become a challenging task. Research papers in a citation network often form communities based on an under lying concept. Our research shows that dispersion based similarity measure can be used as a strong measure for finding similar papers based on similar connectivity in those communities and structural relevance of the citation network. Our results show that our approach works better than other conventional link-based similarity measures both quantitatively and qualitatively.One of the direct benefits of this research is to support the highly specialised information needs of a scholarly researcher working in a specialised field of research.

Abstract

Energy utilization can be improved by precise plug load monitoring and control. Plug load energy consumption is nearly 30% of the total building energy consumption. Therefore, plug load identification is a key requirement for energy conservation in buildings. Intrusive load monitoring techniques identify loads precisely but have not been tested widely so far for their performance in changing operating conditions. Hence, the present research proposes a robust low frequency intrusive load monitoring technique to identify load accurately. A smart power strip using proposed load identification technique is designed and developed. Linear regression is applied on the acquired data to capture the behavioral trends of a particular device more explicitly and concisely. Further, weighted K-NN classifier is applied on the transformed data set for device. Experimental results show that the proposed algorithm performs better than the standard classifiers, and can offer tangible savings.