Giambattista Amati

Query Difficulty, Robustness, and Selective Application of Query Expansion

There is increasing interest in improving the robustness of IR systems, i.e. their effectiveness on difficult queries. A system is robust when it achieves both a high Mean Average Precision (MAP) value for the entire set of topics and a significant MAP value over its worst X topics (MAP(X)). It is a well known fact that Query Expansion (QE) increases global MAP but hurts the performance on the worst topics. A selective application of QE would thus be a natural answer to obtain a more robust retrieval system.

Frequentist and bayesian approach to information retrieval

We introduce the hypergeometric models KL, DLH and DLLH using the DFR approach, and we compare these models to other relevant models of IR. The hypergeometric models are based on the probability of observing two probabilities: the relative within-document term frequency and the entire collection term frequency. Hypergeometric models are parameter-free models of IR. Experiments show that these models have an excellent performance with small and very large collections. We provide their foundations from the same IR probability space of language modelling (LM). We finally discuss the difference between DFR and LM. Briefly, DFR is a frequentist (Type I), or combinatorial approach, whilst language models use a Bayesian (Type II) approach for mixing the two probabilities, being thus inherently parametric in its nature.

Automatic construction of an opinion-term vocabulary for ad hoc retrieval

We present a method to automatically generate a term-opinion lexicon. We also weight these lexicon terms and use them at real time to boost the ranking with opinionated-content documents. We define very simple models both for opinion-term extraction and document ranking. Both the lexicon model and retrieval model are assessed. To evaluate the quality of the lexicon we compare performance with a well-established manually generated opinion-term dictionary. We evaluate the effectiveness of the term-opinion lexicon using the opinion task evaluation data of the TREC 2007 blog track.

A uniform tableau method for intuitionistic modal logics I

We present tableau systems and sequent calculi for the intuitionistic analoguesIK, ID, IT, IKB, IKDB, IB, IK4, IKD4, IS4, IKB4, IK5, IKD5, IK45, IKD45 andIS5 of the normal classical modal logics. We provide soundness and completeness theorems with respect to the models of intuitionistic logic enriched by a modal accessibility relation, as proposed by G. Fischer Servi. We then show the disjunction property forIK, ID, IT, IKB, IKDB, IB, IK4, IKD4, IS4, IKB4, IK5, IK45 andIS5. We also investigate the relationship of these logics with some other intuitionistic modal logics proposed in the literature.

On relevance, time and query expansion

We present the results of our exploratory analysis on the relationship that exists between relevance and time. We observe how the amount of documents published in a given interval of time is related to the probability of relevance, and, using the time series analysis, we show the existence of a correlation between time and relevance. As an initial application of this analysis, we study query expansion exploiting the detection of publication time peaks over the Blog06 collection. We finally propose an effective approach for the query expansion in the blog search domain. Our approach is based on the documents publication trend being so completely independent of any external resource.

On performance of topical opinion retrieval

We investigate the effectiveness of both the standard evaluation measures and the opinion component for topical opinion retrieval. We analyze how relevance is affected by opinions by perturbing relevance ranking by the outcomes of opinion-only classifiers built by Monte Carlo sampling. Topical opinion rankings are obtained by either re-ranking or filtering the documents of a first-pass retrieval of topic relevance. The proposed approach establishes the correlation between the accuracy and the precision of the classifier and the performance of the topical opinion retrieval. Among other results, it is possible to assess the effectiveness of the opinion component by comparing the effectiveness of the relevance baseline with the topical opinion ranking.

A Uniform Theoretic Approach to Opinion and Information Retrieval

In this paper, we introduce a supervised method for the generation of a dictionary of weighted opinion bearing terms from a collection of opinionated documents. We also describe how such a dictionary is used in the framework of an algorithm for opinion retrieval, that is for the problem of identifying the documents in a collection where some opinion is expressed with respect to a given query topic. Several experiments, performed on the TREC Blog collection, are reported together with their results; in these experiments, the use of different combinations of DFR (Divergence from Randomness) probabilistic models to assign weights to terms in the dictionary and to documents is studied and evaluated. The results show the stability of the method and its practical utility. Moreover, we investigate the composition of the generated lexicons, mainly focusing on the presence of stop-words. Quite surprisingly, the best performing dictionaries show a predominant presence of stop-words. Finally, we study the effectiveness of the same approach to generate dictionaries of polarity-bearing terms: preliminary results are provided.

Advances in Information Retrieval

Keynote Talks.- The Next Generation Web Search and the Demise of the Classic IR Model.- The Last Half-Century: A Perspective on Experimentation in Information Retrieval.- Learning in Hyperlinked Environments.- Theory and Design.- A Parameterised Search System.- Similarity Measures for Short Segments of Text.- Multinomial Randomness Models for Retrieval with Document Fields.- On Score Distributions and Relevance.- Modeling Term Associations for Ad-Hoc Retrieval Performance Within Language Modeling Framework.- Efficiency.- Static Pruning of Terms in Inverted Files.- Efficient Indexing of Versioned Document Sequences.- Light Syntactically-Based Index Pruning for Information Retrieval.- Sorting Out the Document Identifier Assignment Problem.- Efficient Construction of FM-index Using Overlapping Block Processing for Large Scale Texts.- Peer-to-Peer Networks (In Memory of Henrik Nottelmann).- Performance Comparison of Clustered and Replicated Information Retrieval Systems.- A Study of a Weighting Scheme for Information Retrieval in Hierarchical Peer-to-Peer Networks.- A Decision-Theoretic Model for Decentralised Query Routing in Hierarchical Peer-to-Peer Networks.- Central-Rank-Based Collection Selection in Uncooperative Distributed Information Retrieval.- Result Merging.- Results Merging Algorithm Using Multiple Regression Models.- Segmentation of Search Engine Results for Effective Data-Fusion.- Queries.- Query Hardness Estimation Using Jensen-Shannon Divergence Among Multiple Scoring Functions.- Query Reformulation and Refinement Using NLP-Based Sentence Clustering.- Automatic Morphological Query Expansion Using Analogy-Based Machine Learning.- Advanced Structural Representations for Question Classification and Answer Re-ranking.- Relevance Feedback.- Incorporating Diversity and Density in Active Learning for Relevance Feedback.- Relevance Feedback Using Weight Propagation Compared with Information-Theoretic Query Expansion.- Evaluation.- A Retrieval Evaluation Methodology for Incomplete Relevance Assessments.- Evaluating Query-Independent Object Features for Relevancy Prediction.- Classification and Clustering.- The Utility of Information Extraction in the Classification of Books.- Combined Syntactic and Semantic Kernels for Text Classification.- Fast Large-Scale Spectral Clustering by Sequential Shrinkage Optimization.- A Probabilistic Model for Clustering Text Documents with Multiple Fields.- Filtering.- Personalized Communities in a Distributed Recommender System.- Information Recovery and Discovery in Collaborative Web Search.- Collaborative Filtering Based on Transitive Correlations Between Items.- Entropy-Based Authorship Search in Large Document Collections.- Topic Identification.- Use of Topicality and Information Measures to Improve Document Representation for Story Link Detection.- Ad Hoc Retrieval of Documents with Topical Opinion.- Expert Finding.- Probabilistic Models for Expert Finding.- Using Relevance Feedback in Expert Search.- XML IR.- Using Topic Shifts for Focussed Access to XML Repositories.- Feature- and Query-Based Table of Contents Generation for XML Documents.- Web IR.- Setting Per-field Normalisation Hyper-parameters for the Named-Page Finding Search Task.- Combining Evidence for Relevance Criteria: A Framework and Experiments in Web Retrieval.- Multimedia IR.- Classifier Fusion for SVM-Based Multimedia Semantic Indexing.- Search of Spoken Documents Retrieves Well Recognized Transcripts.- Short Papers.- Natural Language Processing for Usage Based Indexing of Web Resources.- Harnessing Trust in Social Search.- How to Compare Bilingual to Monolingual Cross-Language Information Retrieval.- Multilingual Text Classification Using Ontologies.- Using Visual-Textual Mutual Information and Entropy for Inter-modal Document Indexing.- A Study of Global Inference Algorithms in Multi-document Summarization.- Document Representation Using Global Association Distance Model.- Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis.- PageRank: When Order Changes.- Model Tree Learning for Query Term Weighting in Question Answering.- Examining Repetition in User Search Behavior.- Popularity Weighted Ranking for Academic Digital Libraries.- Naming Functions for the Vector Space Model.- Effective Use of Semantic Structure in XML Retrieval.- Searching Documents Based on Relevance and Type.- Investigation of the Effectiveness of Cross-Media Indexing.- Improve Ranking by Using Image Information.- N-Step PageRank for Web Search.- Authorship Attribution Via Combination of Evidence.- Posters.- Cross-Document Entity Tracking.- Enterprise People and Skill Discovery Using Tolerant Retrieval and Visualization.- Experimental Results of the Signal Processing Approach to Distributional Clustering of Terms on Reuters-21578 Collection.- Overall Comparison at the Standard Levels of Recall of Multiple Retrieval Methods with the Friedman Test.- Building a Desktop Search Test-Bed.- Hierarchical Browsing of Video Key Frames.- Active Learning with History-Based Query Selection for Text Categorisation.- Fighting Link Spam with a Two-Stage Ranking Strategy.- Improving Naive Bayes Text Classifier Using Smoothing Methods.- Term Selection and Query Operations for Video Retrieval.- An Effective Threshold-Based Neighbor Selection in Collaborative Filtering.- Combining Multiple Sources of Evidence in XML Multimedia Documents: An Inference Network Incorporating Element Language Models.- Language Model Based Query Classification.- Integration of Text and Audio Features for Genre Classification in Music Information Retrieval.- Retrieval Method for Video Content in Different Format Based on Spatiotemporal Features.- Combination of Document Priors in Web Information Retrieval.- Enhancing Expert Search Through Query Modeling.- A Hierarchical Consensus Architecture for Robust Document Clustering.- Summarisation and Novelty: An Experimental Investigation.- A Layered Approach to Context-Dependent User Modelling.- A Bayesian Approach for Learning Document Type Relevance.

On the Retweet Decay of the Evolutionary Retweet Graph

Topological and structural properties of social networks, like Twitter, is of a major importance in order to understand the nature of user activities, for example how information propagates or how to identify influencing accounts. A deeper analysis of these properties may have a crucial impact on the design of new applications and of existing ones.

Assessing the Quality of Opinion Retrieval Systems

Due to the complexity of topical opinion retrieval systems, standard measures, such as MAP or precision, do not fully succeed in assessing their performances. In this paper we introduce an evaluation framework based on artificially defined opinion classifiers. Using a Monte Carlo sampling, we perturb a relevance ranking by the outcomes of these classifiers and analyse how the opinion retrieval performance changes. In this way it is possible to assess the performance of an approach to opinion mining from that of the overall system and to clarify how relevance and opinion are affected by each other.