Abstract

Focused crawling has wide number of applications in the area of Information Retrieval. It is a crucial part in building domain specific search engines, personalized search tools and extending digital libraries. Be it Google Scholar to search for scholarly articles or Google news to search for news articles, domain specific search is the most widely acclaimed application of focused crawling. Unfortunately, there are very few domain specific search engines available for Indian languages.

Abstract

With the growth of web 2.0, people are using it as a medium to express their opinion and thoughts. With the explosion of blogs, journal like user-generated content on the web, companies, celebrities and politicians are concerned about mining and analyzing the discussions about them or their products. In this paper, we present a method to perform opinion mining and summarize opinions at entity level for English blogs. We first identify various objects (named entities) which are talked about by the blogger, then we identify the modifiers which modify the orientation towards these objects. Finally, we generate object centric opinionated summary from blogs. We perform experiments like named entity identification, entity-modifier relationship extraction and modifier orientation estimation. Experiments and Results presented in this paper are cross verified with the judgment of human annotators.

Abstract

Microblogs such as Facebook, Twitter, Google+ present a nice opportunity for advertising on these blogs that are contextually related to the microblog content. The challenge with advertising on such microblog platforms is that there is very little content to classify the tweet as suitable for advertising, which makes it a very hard classification problem. Despite the importance of this area, little formal published research exists. In this work, we try to address the problem of identifying the microblogs that could be targeted for advertisements. We describe a system that learns to classify which microblogs are suitable for targeted advertising. The experiments are conducted on Twitter data. The system user features derived from the user data, the tweet content and uses external resources such as query logs and term frequency from previous twitter data.

Abstract

Summarization has emerged as an increasingly useful approach to tackle the problem of information overload. Extracting information from online conversations can be of very good commercial and educational value. But majority of this information is present as noisy unstructured text making traditional document summarization techniques difficult to apply. In this paper, we propose a novel approach to address the problem of conversation summarization. We develop an automatic text summarizer which extracts sentences from the conversation to form a summary. Our approach consists of three phases. In the first phase, we prepare the dataset for usage by correcting spellings and segmenting the text. In the second phase, we represent each sentence by a set of predefined features. These features capture the statistical, linguistic and sentimental aspects along with the dialogue structure of the conversation. Finally, in the third phase we use a machine learning algorithm to train the summarizer on the set of feature vectors. Experiments performed on conversations taken from the technical domain show that our system significantly outperforms the baselines on ROUGE F-scores.

Abstract

Similar to the traditional approach, we consider the task of summarization as selection of top ranked sentences from ranked sentence-clusters. To achieve this goal, we rank the sentence clusters by using the importance of words calculated by using page rank algorithm on reverse directed word graph of sentences. Next, to rank the sentences in every cluster we introduce the use of weighted clustering coefficient. We use page rank score of words for calculation of weighted clustering coefficient. Finally the most important issue is the presence of a lot of noisy entries in the text, which downgrades the performance of most of the text mining algorithms. To solve this problem, we introduce the use of Wikipedia anchor text based phrase mapping scheme. Our experimental results on DUC-2002 and DUC-2004 dataset show that our system performs better than unsupervised systems and better than/comparable with novel supervised systems of this area.

Abstract

The use of predefined phrase patterns like: N-grams (N>=2), longest common sub sequences or pre defined linguistic patterns etc do not give any credit to non-matching/smaller-size useful patterns and thus, may result in loss of information. Next, the use of 1-gram based model results in several noisy matches. Additionally, due to presence of more than one topic with different levels of importance in summary, we consider summarization evaluation task as topic based evaluation of information content. Means at first stage, we identify the topics covered in given model/reference summary and calculate their importance. At the next stage, we calculate the information coverage in test / machine generated summary, w.r.t. every identified topic. We introduce a graph based mapping scheme and the concept of closeness centrality measure to calculate the information depth and sense of the co-occurring words in every identified topic. Our experimental results show that devised system is better than/comparable with best results of TAC 2011 AESOP dataset.

Abstract

espite the advancement in the information extraction area, the task of identifying associated relations among named entities within a text document remains a significant challenge. Existing automated approaches lack human precision and they also struggle to handle erroneous documents. In this paper, we propose a crowdsourcing-based approach to improve the accuracy of the generated relations from the existing extraction techniques. Our idea is to gather judgments on the extracted relations of an article from the interested users. By contributing, the users in return remember the facts related to a document. This paper presents the complete design of the approach along with a user study done with twelve participants. Results show that the users rated the proposed system positively and were willing to contribute their time and energy for the task.

Abstract

In web search, understanding the intent behind a user query can help in tasks such as placing of ads relevant to the query, routing the query to an appropriate vertical search and building a user profile for personalization. Such an intent could be represented by categories of the information a user is looking for, often expressed through a short query. In this paper, we address query categorization, which involves classifying a given query into one or more pre-defined categories. We propose an information retrieval based approach similar to document retrieval to solve query categorization. For a given query, we retrieve and rank the categories just as in document retrieval, effectively resulting in query categorization. Unlike previous works, the simplicity of the proposed approach makes it practical in a web search scenario, while achieving performance comparable with other systems, when evaluated on KDD Cup 20051 data set. Further, we also report an improvement of 4.2% in terms of precision at position 1, when compared with the best results of KDD Cup 2005.

Abstract

The Text Analysis Conference MultiLing Pilot of 2011 posed a multi-lingual summarization task to the summarization community, aiming to quantify and measure the performance of multi-lingual, multi-document summarization systems. The task was to create a 240–250 word summary from 10 news texts, describing a given topic. The texts of each topic were provided in seven languages (Arabic, Czech, English, French, Greek, Hebrew, Hindi) and each participant generated summaries for at least 2 languages. The evaluation of the summaries was performed using automatic (AutoSummENG, Rouge) and manual processes (Overall Responsiveness score). The participating systems were 8, some of which providing summaries across all languages. This paper provides a brief description for the collection of the data, the evaluation methodology, the problems and challenges faced, and an overview of participation and corresponding results.

Abstract

This paper proposes an approach of extracting simple and effective features that enhances multilingual document ranking (MLDR). There is limited prior research on capturing the concept of multilingual document similarity in determining the ranking of documents. However, the literature available has worked heavily with language specific tools, making them hard to reimplement for other languages. Our approach extracts various multilingual and monolingual similarity features using a basic language resource (bilingual dictionary). No language-specific tools are used, hence making this approach extensible for other languages. We used the datasets provided by Forum for Information Retrieval Evaluation (FIRE) for their 2010 Adhoc Cross-Lingual document retrieval task on Indian languages. Experiments have been performed with different ranking algorithms and their results are compared. The results obtained showcase the effectiveness of the features considered in enhancing multilingual document ranking.