Abstract

Information on web in various languages is growing fast, but large amount of content still exists in English. There are several cases of English text re-use (cross language plagiarism) observed in non-English languages. Detecting text re-use in non-English languages is a challenging task due to complexity of the language used. Complexity further increases for less resource languages like Arabic and Indian languages. In this paper, we address the problem proposed in FIRE CL!TR 2011 task of detecting plagiarized documents in Hindi language which was reused from English language source documents. We proposed three approaches using classification and key-phrase retrieval techniques. Our winning approach attained 0.792 F-measure.

Abstract

This paper presents a novel answer summarization approach for community Question Answering services (cQAs) to address the problem of “incomplete answer”, i.e., missing valuable information from the “best answer” of a complex multi-sentence question, which can be obtained from other answers to the same question. Our method automatically generate a novel and non-redundant summary from cQA answers using structured determinantal point processes (SDPP). Experimental evaluation on sample dataset from Yahoo Answers shows significant improvement over baseline approaches.

Abstract

This paper describes the traditional authorship attribution subtask of the PAN/CLEF 2013 workshop. In our attempt to classify the documents based on gender and age of an author, we have applied a traditional approach of topic modeling using Latent Dirichlet Allocation [LDA]. We used the content based features like topics and style based features like preposition-frequencies, which act as the efficient markers to demarcate the authorship attributes based on age and gender. We demonstrated tenfold cross validation and observed that our classification approach using Maxent and LDA gave an accuracy of 53.3% for English language and 52% for Spanish Language.

Abstract

The ever increasing data on world wide web calls for the use of vertical search engines. Sandhan is one such search engine which offers search in tourism and health genres in more than 10 different Indian languages. In this work we build a URL based genre identification module for Sandhan. A direct impact of this work is on building focused crawlers to gather Indian language content. We conduct experiments on tourism and health web pages in Hindi language. We experiment with three approaches-list based, naive Bayes and incremental naive Bayes. We evaluate our approaches against another web page classification algorithm built on the parsed text of manually labeled web pages. We find that incremental naive Bayes approach outperforms the other two. While doing our experiments we work with different features like words, n-grams and all grams. Using n-gram features we achieve classification accuracies of 0.858 and 0.873 for tourism and health genres respectively.

Abstract

The microblogging platforms are increasingly becoming a lucrative prospect for advertisers to attract the customers. The challenge with advertising on such platforms is that there is very little content to retrieve relevant ads. As the microblogging content is short and noisy and the ads are short too, there is a high amount of lexical/vocabulary mismatch between the micropost and the ads. To bridge this vocabulary mismatch, we propose a conceptual approach that transforms the content into a conceptual space that represent the latent concepts of the content. We empirically show that the conceptual model performs better than various state-of-the-art techniques the performance gain obtained are substantial and significant.

Abstract

In this paper, we propose a novel approach to address the problem of chat summarization. We summarize real-time chat conversations which contain multiple users with frequent shifts in topic. Our approach consists of two phases. In the first phase, we leverage topic modeling using web documents to find the primary topic of discussion in the chat. Then, in the summary generation phase, we build a semantic word space to score sentences based on their association with the primary topic. Experimental results show that our method significantly outperforms the baseline systems on ROUGE F-scores.

Abstract

Content discovery is fast becoming the preferred tool for user engagement on the web. Discovery allows users to get educated and entertained about their topics of interest. StumbleUpon is the largest personalized content discovery engine on the Web, delivering more than 1 billion personalized recommendations per month. As a recommendation system one of the primary metrics we track is whether the user returns (retention) to use the product after their initial experience (session) with StumbleUpon

Abstract

In this paper, we describe our system for the SemEval-2013 Task 2, Sentiment Analysis in Twitter. We formed features that take into account the context of the expression and take a supervised approach towards subjectivity and polarity classification. Experiments were performed on the features to find out whether they were more suited for subjectivity or polarity Classification. We tested our model for sentiment polarity classification on Twitter as well as SMS chat expressions, analyzed their F-measure scores and drew some interesting conclusions from them. Total citations Cited by 3

Abstract

Building bilingual dictionaries from Wikipedia has been extensively studied in the area of computation linguistics. These dictionaries play a crucial role in Natural Language Processing (NLP) applications like Cross-Lingual Information Retrieval, Machine Translation and Named Entity Recognition. To build these dictionaries, most of the existing approaches use information present in Wikipedia titles, info-boxes and categories. Interestingly, not many use the structural properties of a document like sections, subsections, etc. In this work we exploit the structural properties of documents to build a bilingual English-Hindi dictionary. The main intuition behind this approach is that documents in different languages discussing the same topic are likely to have similar structural elements. Though we present our experiments only for Hindi, our approach is language independent and can be easily extended to other languages. The major contribution of our work is that the dictionary contains translation and transliteration of words which include Named Entities to a large extent. We evaluate our dictionary using manually computed precision. We generated a massive list of 72k tokens using our approach with 0.75 precision.

Abstract

Building bilingual dictionaries from Wikipedia has been extensively studied in the area of computation linguistics. These dictionaries play a crucial role in Natural Language Processing(NLP) applications like Cross-Lingual Information Retrieval, Machine Translation and Named Entity Recognition. To build these dictionaries, most of the existing approaches use information present in Wikipedia titles, info-boxes and categories. Interestingly, not many use the structural properties of a document like sections, subsections, etc. In this work we exploit the structural properties of documents to build a bilingual English-Hindi dictionary. The main intuition behind this approach is that documents in different languages discussing the same topic are likely to have similar structural elements. Though we present our experiments only for Hindi, our approach is language independent and can be easily extended to other languages. The major contribution of our work is that the dictionary contains translation and transliteration of words which include Named Entities to a large extent. We evaluate our dictionary using manually computed precision. We generated a massive list of 72k tokens using our approach with 0.75 precision.