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.

Abstract

Blogs has been one of the important resources of information on the internet. Now-a-days lot of Indian language content being generated in the form of blogs. People express their opinions on various situations and events. The content in the blogs may contain named entities–names of people, places, and organizations. Named entities also contain names of eminent personalities who are famous in or out of that language community. The goal of this paper is to find the influence of a personality among cross-language bloggers. The approach we follow is to collect information from blog pages and index the named entities along with their probabilities of occurrence by removing irrelevant information from the blog. When user searches to find the influence of a personality through a query in Indian language, we use a cross language lexicon in the form of multiple language parallel lists to transliterate the query into other Indian languages and mine blogs to return the influence of the personality across Indian language bloggers. An overview of the system and preliminary results are described.

Abstract

Cross lingual information access (CLIA) systems are required to access the large amounts of multilingual content generated on the world wide web in the form of blogs, news articles and documents. In this paper, we discuss our approach to query formation for CLIA systems where language resources are replaced by Wikipedia. We claim that Wikipedia, with its rich multilingual content and structure, forms an ideal platform to build a CLIA system. Our approach is particularly useful for under-resourced languages, as all the languages don’t have the resources (tools) with sufficient accuracies. We propose a context aware language-independent query formation method which, with the help of bilingual dictionaries, forms queries in the target language. Results are encouraging with a precision of 69.75% and thus endorse our claim on using Wikipedia for building CLIA systems.

Abstract

With the growth of social media, document sentiment classification has become an active area of research in this decade. It can be viewed as a special case of topical classification applied only to subjective portions of a document (sources of sentiment). Hence, the key task in document sentiment classification is extracting subjectivity. Existing approaches to extract subjectivity rely heavily on linguistic resources such as sentiment lexicons and complex supervised patterns based on part-of-speech (POS) information. This makes the task of subjective feature extraction complex and resource dependent. In this work, we try to minimize the dependency on linguistic resources in sentiment classification. We propose a simple and statistical methodology called review summary (RSUMM) and use it in combination with well-known feature selection methods to extract subjectivity. Our experimental results on a movie review dataset prove the effectiveness of the proposed methodology.

Abstract

This paper presents Multilingual Document Clustering (MDC) on comparable corpora. Wikipedia has evolved to be a major structured multilingual knowledge base. It has been highly exploited in many monolingual clustering approaches and also in comparing multilingual corpora. But there is no prior work which studied the impact of Wikipedia on MDC. Here, we have studied availing Wikipedia in enhancing MDC performance. We have leveraged Wikipedia knowledge structure (such as cross-lingual links, category, outlinks, Infobox information, etc.) to enrich the document representation for clustering multilingual documents. We have implemented Bisecting k-means clustering algorithm and experiments are conducted on a standard dataset provided by FIRE for their 2010 Ad-hoc Cross-Lingual document retrieval task on Indian languages. We have considered English and Hindi datasets for our experiments. By avoiding language-specific tools, our approach provides a general framework which can be easily extendable to other languages. The system was evaluated using F-score and Purity measures and the results obtained were encouraging.

Abstract

Invalidity search poses different challenges when compared to conventional Information Retrieval problems. Presently, the success of invalidity search relies on the queries created from a patent application by the patent examiner. Since a lot of time is spent in constructing relevant queries, automatically creating them from a patent would save the examiner a lot of effort. In this paper, we address the problem of automatically creating queries from an input patent. An optimal query can be formed by extracting important keywords or phrases from a patent by using Key Phrase Extraction (KPE) techniques. Several KPE algorithms have been proposed in the literature but their performance on query construction for patents has not yet been explored. We systematically evaluate and analyze the performance of queries created by using state-of-the-art KPE techniques for invalidity search task. Our experiments show that t queries formed by KPE approaches perform better than those formed by selecting phrases based on tf or tf-idf scores.

Abstract

This paper presents Multilingual Document Clustering (MDC) using Wikipedia on comparable corpora. Particularly, we utilized the cross lingual links, category, outlinks, Infobox information present in Wikipedia to enrich the document representation. We have used Bisecting k-means algorithm for clustering multilingual documents based on the document similarities. Experiments are conducted based on the usage of English and Hindi Wikipedia. We have considered English and Hindi Datasets provided by FIRE’10 for Ad-hoc Cross-Lingual document retrieval task on Indian languages. No language specific tools are used, which makes the proposed approach easily extendable for other languages. The system is evaluated using F-score and Purity measures and the results obtained are encouraging.

Abstract

Verbose web queries are often descriptive in nature where a term based search engine is unable to distinguish between the essential and noisy words, which can result in a drift from the user intent. We present a randomized query reduction technique that builds on an earlier learning to rank based approach. The proposed technique randomly picks only a small set of samples, instead of the exponentially many sub-queries, thus being fast enough to be useful for web search engines, while still covering wide sub-query space.