OpenRaiser/Intern-Atlas huggingface dataset:
Document clustering with cluster refinement and model selection capabilities
Authors: Xin Liu, Yihong Gong, Wei Xu, Shenghuo Zhu
Venue: SIGIR
Year: 2002

In this paper, we propose a document clustering method that strives to achieve: (1) a high accuracy of document clustering, and (2) the capability of estimating the number of clusters in the document corpus (i.e. the model selection capability). To accurately cluster the given document corpus, we employ a richer feature set to represent each document, and use the Gaussian Mixture Model (GMM) together with the Expectation-Maximization (EM) algorithm to conduct an initial document clustering. From this initial result, we identify a set of discriminative featuresfor each cluster, and refine the initially obtained document clusters by voting on the cluster label of each document using this discriminative feature set. This self-refinement process of discriminative feature identification and cluster label voting is iteratively applied until the convergence of document clusters. On the other hand, the model selection capability is achieved by introducing randomness in the cluster initialization stage, and then discovering a value C for the number of clusters N by which running the document clustering process for a fixed number of times yields sufficiently similar results. Performance evaluations exhibit clear superiority of the proposed method with its improved document clustering and model selection accuracies. The evaluations also demonstrate how each feature as well as the cluster refinement process contribute to the document clustering accuracy.
A critical examination of TDT's cost function
Authors: R. Manmatha, Ao Feng, James Allan
Venue: SIGIR
Year: 2002

Topic Detection and Tracking (TDT) tasks are evaluated using a cost function. The standard TDT cost function assumes a constant probability of relevance P(rel) across all topics. In practice, P(rel) varies widely across topics. We argue using both theoretical and experimental evidence that the cost function should be modified to account for the varying P(rel).
Converting on-line bilingual dictionaries from human-readable to machine-readable form
Authors: James Mayfield, Paul McNamee
Venue: SIGIR
Year: 2002

We describe a language called ABET that allows rapid conversion of on-line human-readable bilingual dictionaries to machinereadable form.
Modeling (in)variability of human judgments for text summarization
Authors: Tadashi Nomoto, Yuji Matsumoto
Venue: SIGIR
Year: 2002

The paper proposes and empirically motivates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. In particular, we explore the use of probabilistic decision tree within the clustering framework to account for the variation as well as regularity in human created summaries.
Analysis of lexical signatures for finding lost or related documents
Authors: Seung-Taek Park, David M. Pennock, C. Lee Giles, Robert Krovetz
Venue: SIGIR
Year: 2002

A lexical signature of a web page is often sufficient for finding the page, even if its URL has changed. We conduct a largescale empirical study of eight methods for generating lexi- cal signatures, including Phelps and Wilensky&apos;s [14] original proposal (PW) and seven of our own variations. We exmnine their performance on the web and on a TREC data set, evaluating their ability both to uniquely identify the origi- nal document and to locate other relevant documents if the original is lost. Lexical signatures chosen to minimize document frequency (DF) are good at unique identification but poor at finding relevant documents. PW works well on the relatively small TREC data set, but acts almost identically to DF on the web, which contains billions of documents. Term-frequency-based lexical signatures (TF) are very easy to compute and often perform well, but are highly dependent on the ranking system of the search engine used. In general, TFIDF-based method and hybrid methods (which combine DF with TF or TFIDF) seem to be the most promising candidates for generating effective lexical signatures.
Set-based model: a new approach for information retrieval
Authors: Bruno Pôssas, Nivio Ziviani, Wagner Meira Jr., Berthier A. Ribeiro-Neto
Venue: SIGIR
Year: 2002

The objective of this paper is to present a new technique for computing term weights for index terms, which leads to a new ranking mechanism, referred to as set-based model. The components in our model are no longer terms, but termsets. The novelty is that we compute term weights using a data mining technique called association rules, which is time efficient and yet yields nice improvements in retrieval effectiveness. The set-based model function for computing the similarity between a document and a query considers the termset frequency in the document and its scarcity in the document collection. Experimental results show that our model improves the average precision of the answer set for all three collections evaluated. For the TReC-3 collection, our set-based model led to a gain, relative to the standard vector space model, of 37% in average precision curves and of 57% in average precision for the top 10 documents. Like the vector space model, the set-based model has time complexity that is linear in the number of documents in the collection.
Content-based music indexing and organization
Authors: Andreas Rauber, Elias Pampalk, Dieter Merkl
Venue: SIGIR
Year: 2002

While electronic music archives are gaining popularity, access to and navigation within these archives is usually limited to text-based queries or manually predefined genre category browsing. We present a system that automatically organizes a music collection according to the perceived sound similarity resembling genres or styles of music. Audio signals are processed according to psychoacoustic models to obtain a time-invariant representation of its characteristics. Subsequent clustering provides an intuitive interface where similar pieces of music are grouped together on a map display.
Compression of inverted indexes for fast query evaluation
Authors: Falk Scholer, Hugh E. Williams, John Yiannis, Justin Zobel
Venue: SIGIR
Year: 2002

Compression reduces both the size of indexes and the time needed to evaluate queries. In this paper, we revisit the compression of inverted lists of document postings that store the position and frequency of indexed terms, considering two approaches to improving retrieval efficiency: better implementation and better choice of integer compression schemes. First, we propose several simple optimisations to well-known integer compression schemes, and show experimentally that these lead to significant reductions in time. Second, we explore the impact of choice of compression scheme on retrieval efficiency.In experiments on large collections of data, we show two surprising results: use of simple byte-aligned codes halves the query evaluation time compared to the most compact Golomb-Rice bitwise compression schemes; and, even when an index fits entirely in memory, byte-aligned codes result in faster query evaluation than does an uncompressed index, emphasising that the cost of transferring data from memory to the CPU cache is less for an appropriately compressed index than for an uncompressed index. Moreover, byte-aligned schemes have only a modest space overhead: the most compact schemes result in indexes that are around 10% of the size of the collection, while a byte-aligned scheme is around 13%. We conclude that fast byte-aligned codes should be used to store integers in inverted lists.
Robust temporal and spectral modeling for query By melody
Authors: Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer
Venue: SIGIR
Year: 2002

Query by melody is the problem of retrieving musical performances from melodies. Retrieval of real performances is complicated due to the large number of variations in performing a melody and the presence of colored accompaniment noise. We describe a simple yet effective probabilistic model for this task. We describe a generative model that is rich enough to capture the spectral and temporal variations of musical performances and allows for tractable melody retrieval. While most of previous studies on music retrieval from melodies were performed with either symbolic (e.g. MIDI) data or with monophonic (single instrument) performances, we performed experiments in retrieving live and studio recordings of operas that contain a leading vocalist and rich instrumental accompaniment. Our results show that the probabilistic approach we propose is effective and can be scaled to massive datasets.
Experiments on data fusion using headline information
Authors: Xiao Mang Shou, Mark Sanderson
Venue: SIGIR
Year: 2002

This poster describes initial work exploring a relatively unexamined area of data fusion: fusing the results of retrieval systems whose collections have no overlap between them. Many of the effective meta-search/data fusion strategies gain much of their success from exploiting document overlap across the source systems being merged. When the intersection of the collections is the empty set, the strategies generally degrade to a simpler form. In order to address such situations, two strategies were examined: re-ranking of merged results using a locally run search on the text fragments returned by the source search engines; and re-ranking based on cross document similarity, again using text fragments presented in the retrieved list. Results, from experiments, which go beyond previous work, indicate that both strategies improve fusion effectiveness.
Using sampled data and regression to merge search engine results
Authors: Luo Si, James P. Callan
Venue: SIGIR
Year: 2002

This paper addresses the problem of merging results obtained from different databases and search engines in a distributed information retrieval environment. The prior research on this problem either assumed the exchange of statistics necessary for normalizing scores (cooperative solutions) or is heuristic. Both approaches have disadvantages. We show that the problem in uncooperative environments is simpler when viewed as a component of a distributed IR system that uses query-based sampling to create resource descriptions. Documents sampled for creating resource descriptions can also be used to create a sample centralized index, and this index is a source of training data for adaptive results merging algorithms. A variety of experiments demonstrate that this new approach is more effective than a well-known alternative, and that it allows query-by-query tuning of the results merging function.
Does WT10g look like the web?
Authors: Ian Soboroff
Venue: SIGIR
Year: 2002

We measure the WT10g test collection, used in the TREC-9 and TREC 2001 Web Tracks, with common measures used in the web topology community, in order to see if WT10g "looks like" the web. This is not an idle question; characteristics of the web, such as power law relationships, diameter, and connected components have all been observed within the scope of general web crawls, constructed by blindly following links. In contrast, WT10g was carved out from a larger crawl specifically to be a web search test collection within the reach of university researchers. Does such a collection retain the properties of the larger web? In the case of WT10g, yes.
Query performance analyser: a web-based tool for IR research and instruction
Authors: Eero Sormunen, Sakari Hokkanen, Petteri Kangaslampi, Petri Pyy, Bemmu Sepponen
Venue: SIGIR
Year: 2002

The Interactive Query Performance Analyser (QPA) for information retrieval systems is a Web-based tool for analysing and comparing the performance of individual queries. On top of a standard test collection, it gives an instant visualisation of the performance achieved in a given search topic by any user-generated query. In addition to experimental IR research, QPA can be used in user training to demonstrate the characteristics of and compare differences between IR systems and searching strategies. The first prototype (versions 3.0 and 3.5) of the Query Performance Analyser was developed at the Department of Information Studies, University of Tampere, to serve as a tool for rapid query performance analysis, comparison and visualisation [4,5]. Later, it has been applied to interactive optimisation of queries [2,3]. The analyser has served also in learning environments for IR [1].The demonstration is based on the newest version of the Query Performance Analyser (v. 5.1). It is interfaced to a traditional Boolean IR system (TRIP) and a probabilistic IR system (Inquery) providing access to the TREC collection and two Finnish test collections. Version 5.1 supports multigraded relevance scales, new types of performance visualisations, and query conversions based on mono- and multi-lingual dictionaries. The motivation in developing the analyser is to emphasise the necessity of analysing the behaviour of individual queries. Information retrieval experiments usually measure the average effectiveness of IR methods developed. The analysis of individual queries is neglected although test results may contain individual test topics where general findings do not hold. For the real user of an IR system, the study of variation in results is even more important than averages.
Souvenir: flexible note-taking tool to pinpoint and share media highlights
Authors: Anselm Spoerri
Venue: SIGIR
Year: 2002

Digital media audio/video can be difficult to search and share in a personal way. Souvenir is a software system that offers users a flexible and comprehensive way to use their handwritten or text notes to retrieve and share specific media moments. Users can take notes on a variety of devices, such as the paper-based CrossPad, the Palm Pilot and standard keyboard devices. Souvenir segments handwritten notes into an effective media index without the need for handwriting recognition. Users can use their notes to create hyperlinks to random-access media stored in a digital library. Souvenir also has web publishing and email capabilities to enable anyone to access or email media moments directly from a web page. Souvenir annotations capture information that can not be easily inferred by automatic media indexing tools.
Selecting indexing strings using adaptation
Authors: Yoshiyuki Takeda, Kyoji Umemura
Venue: SIGIR
Year: 2002

It is not easy to tokenize agglutinative languages like Japanese and Chinese into words. Many IR systems start with a dictionary-based morphology program like ChaSen [4]. Unfortunately, dictionaries cannot cover all possible words; unknown words such as proper nouns are important for IR. This paper proposes a statistical dictionary-free method for selecting index strings based on recent work on adaptive language modeling.
Error correction in a Chinese OCR test collection
Authors: Yuen-Hsien Tseng
Venue: SIGIR
Year: 2002

This article proposes a technique for correcting Chinese OCR errors to support retrieval of scanned documents. The technique uses a completely automatic technique (no manually constructed lexicons or confusion resources) to identify both keywords and confusable terms. Improved retrieval effectiveness on a single term query experiment is demonstrated.
K-tree/forest: efficient indexes for boolean queries
Authors: Rakesh M. Verma, Sanjiv Behl
Venue: SIGIR
Year: 2002

In Information Retrieval it is well-known that the complexity of processing boolean queries depends on the size of the intermediate results, which could be huge (and are typically on disk) even though the size of the final result may be quite small. In the case of inverted files the most time consuming operation is the merging or intersection of the list of occurrences [1]. We propose, the Keyword tree (K-tree) and forest, efficient structures to handle boolean queries in keyword-based information retrieval. Extensive simulations show that K-tree is orders-of-magnitude faster (i.e., far fewer I/O's) for boolean queries than the usual approach of merging the lists of occurrences and incurs only a small overhead for single keyword queries. The K-tree can be efficiently parallelized as well. The construction cost of K-tree is comparable to the cost of building inverted files.
Example-based phrase translation in Chinese-English CLIR
Authors: Bin Wang, Xueqi Cheng, Shuo Bai
Venue: SIGIR
Year: 2002

This paper proposes an example-based phrase translation method in a Chinese to English cross-language information retrieval (CLIR) system. The method can generate much more accurate query translations than dictionary-based and common MT-based methods, and then improves the retrieval performance of our CLIR system.
Probabilistic multimedia retrieval
Authors: Thijs Westerveld
Venue: SIGIR
Year: 2002

We present a framework in which probabilistic models for textual and visual information retrieval can be integrated seamlessly. The framework facilitates searching for imagery using textual descriptions and visual examples simultaneously. The underlying Language Models for text and Gaussian Mixture Models for images have proven successful in various retrieval tasks.
Automatic classification in product catalogs
Authors: Ben Wolin
Venue: SIGIR
Year: 2002

In this paper, we present the AutoCat system for product classification. AutoCat uses a vector space model, modified to consider product attributes unavailable in traditional document classification. We present key features of our user interface, developed to assist users with evaluating and editing the output of the classification algorithm. Finally, we present observations about the use of this technology in the field.
Chinese keyword extraction based on max-duplicated strings of the documents
Authors: Wenfeng Yang
Venue: SIGIR
Year: 2002

The corpus analysis methods in Chinese keyword extraction look on the corpus as a single sample of language stochastic process. But the distributions of keywords in the whole corpus and in each document are very different from each other. The extraction based on global statistical information only can get significant keywords in the whole corpus. Max-duplicated strings contain the local significant keywords in each document. In this paper, we designed an efficient algorithm to extract the max-duplicated strings by building PAT-tree for the document, so that the keywords can be picked out from the max-duplicated strings by their SIG values in the corpus.
Correlating multilingual documents via bipartite graph modeling
Authors: Hongyuan Zha, Xiang Ji
Venue: SIGIR
Year: 2002

There is enormous amount of multilingual documents from various sources and possibly from different countries describing a single event or a set of related events. It is desirable to construct text mining methods that can compare and highlight similarities and differences of those multilingual documents. We discuss our ongoing research that seeks to model a pair of multilingual documents as a weighted bipartite graph with the edge weights computed by means of machine translation. We use spectral method to identify dense subgraphs of the weighted bipartite graph which can be considered as corresponding to sentences that correlate well in textual contents. We illustrate our approach using English and German texts.
Two-stage language models for information retrieval
Authors: ChengXiang Zhai, John D. Lafferty
Venue: SIGIR
Year: 2002

The optimal settings of retrieval parameters often depend on both the document collection and the query, and are usually found through empirical tuning. In this paper, we propose a family of two-stage language models for information retrieval that explicitly captures the different influences of the query and document collection on the optimal settings of retrieval parameters. As a special case, we present a two-stage smoothing method that allows us to estimate the smoothing parameters completely automatically. In the first stage, the document language model is smoothed using a Dirichlet prior with the collection language model as the reference model. In the second stage, the smoothed document language model is further interpolated with a query background language model. We propose a leave-one-out method for estimating the Dirichlet parameter of the first stage, and the use of document mixture models for estimating the interpolation parameter of the second stage. Evaluation on five different databases and four types of queries indicates that the two-stage smoothing method with the proposed parameter estimation methods consistently gives retrieval performance that is close to---or better than---the best results achieved using a single smoothing method and exhaustive parameter search on the test data.
HAT: a hardware assisted TOP-DOC inverted index component
Authors: S. Kagan Agun, Ophir Frieder
Venue: SIGIR
Year: 2003

A novel Hardware Assisted Top-Doc (HAT) component is disclosed. HAT is an optimized content indexing device based on a modified inverted index structure. HAT accommodates patterns of different lengths and supports a varied posting list versus term count feature sustaining high reusability and efficiency. The developed component can be used either as an internal slave component or as an external co-processor and is efficient in resource demands as the component controllers take only a minimal percentage of the target device space leaving the majority of the space to term and posting entries. A Very High Speed Integrated Circuit (VHSIC) Hardware Description Language (VHDL) is used to model the HAT system.
Summary evaluation and text categorization
Authors: Khurshid Ahmad, Bogdan Vrusias, Paulo C. F. de Oliveira
Venue: SIGIR
Year: 2003

In general terms the evaluation of a summary depends on how close it is to the chief points in the source text. This begets the question as to what are the chief points in the source text and how is this information used in itself in identifying the source text. This is crucially important when we discuss automatic evaluation of summaries. So the question of main points is the source text. Typically, this would be around a nucleus of keywords. However, the salience, the frequency, and the relationship of the text with other texts in the collection (of these keywords is perhaps) are important. Text categorisation using neural networks explicates these points well and also has a practical impact.
A light weight PDA-friendly collection fusion technique
Authors: Jeffery Antoniuk, Mario A. Nascimento
Venue: SIGIR
Year: 2003

This short paper presents a light weight technique to merge results lists obtained from querying different databases. The motivation for such a technique is a general purpose search engine for Palm-OS based PDAs.
Discovering and structuring information flow among bioinformatics resources
Authors: Joan C. Bartlett, Elaine G. Toms
Venue: SIGIR
Year: 2003

In this poster, we present a model of the flow of information among bioinformatics resources in the context of a specific scientific problem. Combining task analysis with traditional, qualitative research, we determined the extent to which the bioinformatics analysis process could be automated. The model represents a semi-automated process, involving fourteen distinct data processing steps, and forms the framework for an interface to bioinformatics information.
SETS: search enhanced by topic segmentation
Authors: Mayank Bawa, Gurmeet Singh Manku, Prabhakar Raghavan
Venue: SIGIR
Year: 2003

We present SETS, an architecture for efficient search in peer-to-peer networks, building upon ideas drawn from machine learning and social network theory. The key idea is to arrange participating sites in a topic-segmented overlay topology in which most connections are short-distance, connecting pairs of sites with similar content. Topically focused sets of sites are then joined together into a single network by long-distance links. Queries are matched and routed to only the topically closest regions. We discuss a variety of design issues and tradeoffs that an implementor of SETS would face. We show that SETS is efficient in network traffic and query processing load.