Towards automatic generation of relevance judgments for a test collection

This paper represents a new technique for building a relevance judgment list for information retrieval test collections without any human intervention. It is based on the number of occurrences of the documents in runs retrieved from several information retrieval systems and a distance based measure between the documents. The effectiveness of the technique is evaluated by computing the correlation between the ranking of the TREC systems using the original relevance judgment list (qrels) built by human assessors and the ranking obtained by using the newly generated qrels

A Semantic Graph-Based Approach for Mining Common Topics From Multiple Asynchronous Text Streams

In the age of Web 2.0, a substantial amount of unstructured content are distributed through multiple text streams in an asynchronous fashion, which makes it increasingly difficult to glean and distill useful information. An effective way to explore the information in text streams is topic modelling, which can further facilitate other applications such as search, information browsing, and pattern mining. In this paper, we propose a semantic graph based topic modelling approach for structuring asynchronous text streams. Our model in- tegrates topic mining and time synchronization, two core modules for addressing the problem, into a unified model. Specifically, for handling the lexical gap issues, we use global semantic graphs of each timestamp for capturing the hid- den interaction among entities from all the text streams. For dealing with the sources asynchronism problem, local semantic graphs are employed to discover similar topics of different entities that can be potentially separated by time gaps. Our experiment on two real-world datasets shows that the proposed model significantly outperforms the existing ones

Viewing the Dictionary as a Classification System

Information retrieval is one of the earliest applications of computers. Starting with the speculative wode of Vannevar Bush on Memex [Bush 45], to the development of Key Word in Context (KWIC) indexing by H.P. Luhn [Luhn 60] and Boolean retrieval by John Horty [Horty 62], to the statistical techniques for automatic indexing and document retrieval done in the 1960's and continuing to the present [Salton and McGill 83], Information Retrieval has continued to develop and progress. However, there is a growing consensus that current generation statistical techniques have gone about as far as they can go, and that further improvement requires the use of natural language processing and knowledge representation. We believe that the best place to start is by focusing on the lexicon, and to index documents not by words, but by word senses. Why use word senses? Conventional approaches advocate either indexing by the words themselves, or by manual indexing using a controlled vocabulary. Manual indexing offers some of the advantage of word senses, in that the terms are not ambiguous, but it suffers from problems of consistency. In addition, as text data bases continue to grow, it will only be possible to index a fraction of them by hand. In advocating word senses as indices we are not suggesting that they are the ultimate answer. There is much more to the meaning of a document then the senses of the words it contains; we are just saying that senses are a good start. Any approach to providing a semantic analysis must deal with the problem of word meaning. Existing retrieval systems try to go beyond single words by using a thesaurus,l but this has the problem that words are not synonymous in all contexts. The word 'term' may be synonymous with 'word' (as in a vocabulary term), 'sentence' (as in a prison term), or 'condition' (as in 'terms of agreement'). If we expand the query with words from a thesaurus, we must be careful to use the right senses of those words. We not only have to know the sense of the word in the query (in this example, the sense of the word 'term '), but the sense of the word that is being used to augment it (e.g., the appropriate sense of the word 'sentence'). The thesaurus we use should be one in which the senses of words are explicitly indicated [Chodorow et al. 88]. We contend that the best place to obtain word senses is a machine-readable dictionary. Although it is possible that another list of senses might be manually constructed, this strategy might cause some senses to be overlooked, and the task will entail a great degree of effort

Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches

On enhancing the robustness of timeline summarization test collections

Timeline generation systems are a class of algorithms that produce a sequence of time-ordered sentences or text snippets extracted in real-time from high-volume streams of digital documents (e.g. news articles), focusing on retaining relevant and informative content for a particular information need (e.g. topic or event). These systems have a range of uses, such as producing concise overviews of events for end-users (human or artificial agents). To advance the field of automatic timeline generation, robust and reproducible evaluation methodologies are needed. To this end, several evaluation metrics and labeling methodologies have recently been developed - focusing on information nugget or cluster-based ground truth representations, respectively. These methodologies rely on human assessors manually mapping timeline items (e.g. sentences) to an explicit representation of what information a ‘good’ summary should contain. However, while these evaluation methodologies produce reusable ground truth labels, prior works have reported cases where such evaluations fail to accurately estimate the performance of new timeline generation systems due to label incompleteness. In this paper, we first quantify the extent to which the timeline summarization test collections fail to generalize to new summarization systems, then we propose, evaluate and analyze new automatic solutions to this issue. In particular, using a depooling methodology over 19 systems and across three high-volume datasets, we quantify the degree of system ranking error caused by excluding those systems when labeling. We show that when considering lower-effectiveness systems, the test collections are robust (the likelihood of systems being miss-ranked is low). However, we show that the risk of systems being mis-ranked increases as the effectiveness of systems held-out from the pool increases. To reduce the risk of mis-ranking systems, we also propose a range of different automatic ground truth label expansion techniques. Our results show that the proposed expansion techniques can be effective at increasing the robustness of the TREC-TS test collections, as they are able to generate large numbers missing matches with high accuracy, markedly reducing the number of mis-rankings by up to 50%

SEARCHING BASED ON QUERY DOCUMENTS

Searches can start with query documents where search queries are formulated based on document-level descriptions. This type of searches is more common in domain-specific search environments. For example, in patent retrieval, one major search task is finding relevant information for new (query) patents, and search queries are generated from the query patents One unique characteristic of this search is that the search process can take longer and be more comprehensive, compared to general web search. As an example, to complete a single patent retrieval task, a typical user may generate 15 queries and examine more than 100 retrieved documents. In these search environments, searchers need to formulate multiple queries based on query documents that are typically complex and difficult to understand. In this work, we describe methods for automatically generating queries and diversifying search results based on query documents, which can be used for query vi suggestion and for improving the quality of retrieval results. In particular, we focus on resolving three main issues related to query document-based searches: (1) query generation, (2) query suggestion and formulation, and (3) search result diversification. Automatic query generation helps users by reducing the burden of formulating queries from query documents. Using generated queries as suggestions is investigated as a method of presenting alternative queries. Search result diversification is important in domain-specific search because of the nature of the query documents. Since query documents generally contain long complex descriptions, diverse query topics can be identified, and a range of relevant documents can be found that are related to these diverse topics. The proposed methods we study in this thesis explicitly address these three issues. To solve the query generation issue, we use binary decision trees to generate effective Boolean queries and labeling propagation to formulate more effective phrasal-concept queries. In order to diversify search results, we propose two different approaches: query-side and result-level diversification. To generate diverse queries, we identify important topics from query documents and generate queries based on the identified topics. For result-level diversification, we extract query topics from query documents, and apply state-of-the-art diversification algorithms based on the extracted topics. In addition, we devise query suggestion techniques for each query generation method. To demonstrate the effectiveness of our approach, we conduct experiments for various domain-specific search tasks, and devise appropriate evaluation measures for domain-specific search environments

Expertise Profiling in Evolving Knowledgecuration Platforms

Expertise modeling has been the subject of extensiveresearch in two main disciplines: Information Retrieval (IR) andSocial Network Analysis (SNA). Both IR and SNA approachesbuild the expertise model through a document-centric approachproviding a macro-perspective on the knowledge emerging fromlarge corpus of static documents. With the emergence of the Webof Data there has been a significant shift from static to evolvingdocuments, through micro-contributions. Thus, the existingmacro-perspective is no longer sufficient to track the evolution ofboth knowledge and expertise. In this paper we present acomprehensive, domain-agnostic model for expertise profiling inthe context of dynamic, living documents and evolving knowledgebases. We showcase its application in the biomedical domain andanalyze its performance using two manually created datasets

Exploiting Social Media Sources for Search, Fusion and Evaluation

The web contains heterogeneous information that is generated with different characteristics and is presented via different media. Social media, as one of the largest content carriers, has generated information from millions of users worldwide, creating material rapidly in all types of forms such as comments, images, tags, videos and ratings, etc. In social applications, the formation of online communities contributes to conversations of substantially broader aspects, as well as unfiltered opinions about subjects that are rarely covered in public media. Information accrued on social platforms, therefore, presents a unique opportunity to augment web sources such as Wikipedia or news pages, which are usually characterized as being more formal. The goal of this dissertation is to investigate in depth how social data can be exploited and applied in the context of three fundamental information retrieval (IR) tasks: search, fusion, and evaluation. Improving search performance has consistently been a major focus in the IR community. Given the in-depth discussions and active interactions contained in social media, we present approaches to incorporating this type of data to improve search on general web corpora. In particular, we propose two graph-based frameworks, social anchor and information network, to associate related web and social content, where information sources of diverse characteristics can be used to complement each other in a unified manner. We investigate how the enriched representation can potentially reduce vocabulary mismatch and improve retrieval effectiveness. Presenting social media content to users is valuable particularly for queries intended for time-sensitive events or community opinions. Current major search engines commonly blend results from different search services (or verticals) into core web results. Motivated by this real-world need, we explore ways to merge results from different web and social services into a single ranked list. We present an optimization framework for fusion, where impact of documents, ranked lists, and verticals can be modeled simultaneously to maximize performance. Evaluating search system performance has largely relied on creating reusable test collections in IR. Traditional ways to creating evaluation sets can require substantial manual effort. To reduce such effort, we explore an approach to automating the process of collecting pairs of queries and relevance judgments, using high quality social media, Community Question Answering (CQA). Our approach is based on the idea that CQA services support platforms for users to raise questions and to share answers, therefore encoding the associations between real user information needs and real user assessments. To demonstrate the effectiveness of our approaches, we conduct extensive retrieval and fusion experiments, as well as verify the reliability of the new, CQA-based evaluation test sets