OpenRaiser/Intern-Atlas huggingface dataset:
Recognizing surfaces using three-dimensional textons
Authors: Thomas K. Leung, Jitendra Malik
Venue: Conference ICCV 1999
Year: 1999

We study the recognition of surfaces made from different materials such as concrete, rug, marble or leather on the basis of their textural appearance. Such natural textures arise from spatial variation of two surface attributes: (1) reflectance and (2) surface normal. In this paper, we provide a unified model to address both these aspects of natural texture. The main idea is to construct a vocabulary of prototype tiny surface patches with associated local geometric and photometric properties. We call these 3D textons. Examples might be ridges, grooves, spots or stripes or combinations thereof Associated with each texton is an appearance vector, which characterizes the local irradiance distribution, represented as a set of linear Gaussian derivative filter outputs, under different lighting and viewing conditions. Given a large collection of images of different materials, a clustering approach is used to acquire a small (on the order of 100) 3D texton vocabulary. Given a few (1 to 4) images of any material, it can be characterized using these textons. We demonstrate the application of this representation for recognition of the material viewed under novel lighting and viewing conditions.
Einstein: physicist or vegetarian? Summarizing semantic type graphs for knowledge discovery
Authors: Tomasz Tylenda, Mauro Sozio, Gerhard Weikum
Venue: Conference WWW 2011
Year: 2011

The Web and, in particular, knowledge-sharing communities such as Wikipedia contain a huge amount of information encompassing disparate and diverse fields. Knowledge bases such as DBpedia or Yago represent the data in a concise and more structured way bearing the potential of bringing database tools to Web Search. The wealth of data, however, poses the challenge of how to retrieve important and valuable information, which is often intertwined with trivial and less important details. This calls for an efficient and automatic summarization method.
Time-dependent event hierarchy construction
Authors: Gabriel Pui Cheong Fung, Jeffrey Xu Yu, Huan Liu, Philip S. Yu
Venue: Conference KDD 2007
Year: 2007

In this paper, an algorithm called Time Driven Documents-partition (TDD) is proposed to construct an event hierarchy in a text corpus based on a given query. Specifically, assume that a query contains only one feature - Election. Election is directly related to the events such as 2006 US Midterm Elections Campaign, 2004 US Presidential Election Campaign and 2004 Taiwan Presidential Election Campaign, where these events may further be divided into several smaller events (e.g. the 2006 US Midterm Elections Campaign can be broken down into events such as campaign for vote, election results and the resignation of Donald H. Rumsfeld). As such, an event hierarchy is resulted. Our proposed algorithm, TDD, tackles the problem by three major steps: (1)Identify the features that are related to the query according to both the timestamps and the contents of the documents. The features identified are regarded as bursty features; (2) Extract the documents that are highly related to the bursty features based on time; (3) Partition the extracted documents to form events and organize them in a hierarchicalstructure. To the best of our knowledge, there is little works targeting for constructing a feature-based event hierarchy for a text corpus. Practically, event hierarchies can assist us to efficiently locate our target information in a text corpus easily. Again, assume that Election is used for a query. Without an event hierarchy, it is very difficult to identify what are the major events related to it, when do these events happened, as well as the features and the news articles that are related to each of these events. We have archived two-year news articles to evaluate the feasibility of TDD. The encouraging results indicated that TDD is practically sound and highly effective.
Utility scoring of product reviews
Authors: Zhu Zhang, Balaji Varadarajan
Venue: Conference CIKM 2006
Year: 2006

We identify a new task in the ongoing research in text sentiment analysis: predicting utility of product reviews, which is orthogonal to polarity classification and opinion extraction. We build regression models by incorporating a diverse set of features, and achieve highly competitive performance for utility scoring on three real-world data sets.
Effects of the recession on public mood in the uk
Authors: Thomas Lansdall-Welfare, Vasileios Lampos, Nello Cristianini
Venue: Conference WWW 2012
Year: 2012

Large scale analysis of social media content allows for real time discovery of macro-scale patterns in public opinion and sentiment. In this paper we analyse a collection of 484 million tweets generated by more than 9.8 million users from the United Kingdom over the past 31 months, a period marked by economic downturn and some social tensions. Our findings, besides corroborating our choice of method for the detection of public mood, also present intriguing patterns that can be explained in terms of events and social changes. On the one hand, the time series we obtain show that periodic events such as Christmas and Halloween evoke similar mood patterns every year. On the other hand, we see that a significant increase in negative mood indicators coincide with the announcement of the cuts to public spending by the government, and that this effect is still lasting. We also detect events such as the riots of summer 2011, as well as a possible calming effect coinciding with the run up to the royal wedding.
ChatNoir: a search engine for the ClueWeb09 corpus
Authors: Martin Potthast, Matthias Hagen, Benno Stein, Jan Graßegger, Maximilian Michel, Martin Tippmann, Clement Welsch
Venue: Conference SIGIR 2012
Year: 2012

We present the ChatNoir search engine which indexes the entire English part of the ClueWeb09 corpus. Besides Carnegie Mellon's Indri system, ChatNoir is the second publicly available search engine for this corpus. It implements the classic BM25F information retrieval model including PageRank and spam likelihood. The search engine is scalable and returns the first results within three seconds, which is significantly faster than Indri. A convenient API allows for implementing reproducible experiments based on retrieving documents from the ClueWeb09 corpus. The search engine has successfully accomplished a load test involving 100,000 queries.
Query segmentation revisited
Authors: Matthias Hagen, Martin Potthast, Benno Stein, Christof Bräutigam
Venue: Conference WWW 2011
Year: 2011

We address the problem of query segmentation: given a keyword query, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve reasonable segmentation performance but are tested only against a small corpus of manually segmented queries. In addition, many of the previous approaches are fairly intricate as they use expensive features and are difficult to be reimplemented. The main contribution of this paper is a new method for query segmentation that is easy to implement, fast, and that comes with a segmentation accuracy comparable to current state-of-the-art techniques. Our method uses only raw web n-gram frequencies and Wikipedia titles that are stored in a hash table. At the same time, we introduce a new evaluation corpus for query segmentation. With about 50,000 human-annotated queries, it is two orders of magnitude larger than the corpus being used up to now.
Revisiting the dependence language model for information retrieval
Authors: Loïc Maisonnasse, Éric Gaussier, Jean-Pierre Chevallet
Venue: Conference SIGIR 2007
Year: 2007

In this paper, we revisit the dependence language modelfor information retrieval proposed in [1], and show that thismodel is deficient from a theoretical point of view. We thenpropose a new model, well founded theoretically, for integratingdependencies between terms in the language model.This new model is simpler, yet more general, than the oneproposed in [1], and yields similar results in our experiments,on both syntactic and semantic dependencies.
A quasi-synchronous dependence model for information retrieval
Authors: Jae Hyun Park, W. Bruce Croft, David A. Smith
Venue: Conference CIKM 2011
Year: 2011

Incorporating syntactic features in a retrieval model has had very limited success in the past, with the exception of binary term dependencies. This paper presents a new term dependency modeling approach based on syntactic dependency parsing for both queries and documents. Our model is inspired by a quasi-synchronous stochastic process for machine translation[21]. We model four different types of relationships between syntactically dependent term pairs to perform inexact matching between documents and queries. We also propose a machine learning technique for predicting optimal parameter settings for a retrieval model incorporating syntactic relationships. The results on TREC collections show that the quasi-synchronous dependence model can improve retrieval performance and outperform a strong state-of-art sequential dependence baseline when we use predicted optimal parameters.
Generating english summaries of time series data using the gricean maxims
Authors: Somayajulu Sripada, Ehud Reiter, Jim Hunter, Jin Yu
Venue: Conference KDD 2003
Year: 2003

We are developing technology for generating English textual summaries of time-series data, in three domains: weather forecasts, gas-turbine sensor readings, and hospital intensive care data. Our weather-forecast generator is currently operational and being used daily by a meteorological company. We generate summaries in three steps: (a) selecting the most important trends and patterns to communicate; (b) mapping these patterns onto words and phrases; and (c) generating actual texts based on these words and phrases. In this paper we focus on the first step, (a), selecting the information to communicate, and describe how we perform this using modified versions of standard data analysis algorithms such as segmentation. The modifications arose out of empirical work with users and domain experts, and in fact can all be regarded as applications of the Gricean maxims of Quality, Quantity, Relevance, and Manner, which describe how a cooperative speaker should behave in order to help a hearer correctly interpret a text. The Gricean maxims are perhaps a key element of adapting data analysis algorithms for effective communication of information to human users, and should be considered by other researchers interested in communicating data to human users.
Generic summaries for indexing in information retrieval
Authors: Tetsuya Sakai, Karen Sparck Jones
Venue: Conference SIGIR 2001
Year: 2001

This paper examines the use of generic summaries for indexing in information retrieval. Our main observations are that: (1) With or without pseudo-relevance feedback, a summary index may be as effective as the corresponding fulltext index forprecision-oriented search of highly relevant documents. %43 But a reasonably sophisticated summarizer, using a compression ratio of 10-30%, is desirable for this purpose. (2) In pseudo-relevance feedback, using a summary index at initial search and a fulltext index at final search is possibly effective for precision-oriented search, regardless of relevance levels. This strategy is significantly more effective than the one using the summary index only and probably more effective than using summaries as mere term selection filters. %the use of summaries as mere term selection filters. %The summary quality is probably not a critical factor for this strategy, For this strategy, the summary quality is probably not a critical factor, and a compression ratio of 5-10% appears best.
Extracting discriminative concepts for domain adaptation in text mining
Authors: Bo Chen, Wai Lam, Ivor W. Tsang, Tak-Lam Wong
Venue: Conference KDD 2009
Year: 2009

One common predictive modeling challenge occurs in text mining problems is that the training data and the operational (testing) data are drawn from different underlying distributions. This poses a great difficulty for many statistical learning methods. However, when the distribution in the source domain and the target domain are not identical but related, there may exist a shared concept space to preserve the relation. Consequently a good feature representation can encode this concept space and minimize the distribution gap. To formalize this intuition, we propose a domain adaptation method that parameterizes this concept space by linear transformation under which we explicitly minimize the distribution difference between the source domain with sufficient labeled data and target domains with only unlabeled data, while at the same time minimizing the empirical loss on the labeled data in the source domain. Another characteristic of our method is its capability for considering multiple classes and their interactions simultaneously. We have conducted extensive experiments on two common text mining problems, namely, information extraction and document classification to demonstrate the effectiveness of our proposed method. Copyright 2009 ACM.
Stics: searching with strings, things, and cats
Authors: Johannes Hoffart, Dragan Milchevski, Gerhard Weikum
Venue: Conference SIGIR 2014
Year: 2014

This paper describes an advanced search engine that supports users in querying documents by means of keywords, entities, and categories. Users simply type words, which are automatically mapped onto appropriate suggestions for entities and categories. Based on named-entity disambiguation, the search engine returns documents containing the query's entities and prominent entities from the query's categories.
Retrieval of similar chess positions
Authors: Debasis Ganguly, Johannes Leveling, Gareth J. F. Jones
Venue: Conference SIGIR 2014
Year: 2014

We address the problem of retrieving chess game positions similar to a given query position from a collection of archived chess games. We investigate this problem from an information retrieval (IR) perspective. The advantage of our proposed IR-based approach is that it allows using the standard inverted organization of stored chess positions, leading to an efficient retrieval. Moreover, in contrast to retrieving exactly identical board positions, the IR-based approach is able to provide approximate search functionality. In order to define the similarity between two chess board positions, we encode each game state with a textual representation. This textual encoding is designed to represent the position, reachability and the connectivity between chess pieces. Due to the absence of a standard IR dataset that can be used for this search task, a new evaluation benchmark dataset was constructed comprising of documents (chess positions) from a freely available chess game archive. Experiments conducted on this dataset demonstrate that our proposed method of similarity computation, which takes into account a combination of the mobility and the connectivities between the chess pieces, performs well on the search task, achieving MAP and nDCG values of 0:4233 and 0:6922 respectively.
Supporting exploratory people search: a study of factor transparency and user control
Authors: Shuguang Han, Daqing He, Jiepu Jiang, Zhen Yue
Venue: Conference CIKM 2013
Year: 2013

People search is an active research topic in recent years. Related works includes expert finding, collaborator recommendation, link prediction and social matching. However, the diverse objectives and exploratory nature of those tasks make it difficult to develop a flexible method for people search that works for every task. In this project, we developed PeopleExplorer, an interactive people search system to support exploratory search tasks when looking for people. In the system, users could specify their task objectives by selecting and adjusting key criteria. Three criteria were considered: the content relevance, the candidate authoritativeness and the social similarity between the user and the candidates. This project represents a first attempt to add transparency to exploratory people search, and to give users full control over the search process. The system was evaluated through an experiment with 24 participants undertaking four different tasks. The results show that with comparable time and effort, users of our system performed significantly better in their people search tasks than those using the baseline system. Users of our system also exhibited many unique behaviors in query reformulation and candidate selection. We found that users' general perceptions about three criteria varied during different tasks, which confirms our assumptions regarding modeling task difference and user variance in people search systems.
Incorporating phrase-level sentiment analysis on textual reviews for personalized recommendation
Authors: Yongfeng Zhang
Venue: Conference WSDM 2015
Year: 2015

Previous research on Recommender Systems (RS), especially the continuously popular approach of Collaborative Filtering (CF), has been mostly focusing on the information resource of explicit user numerical ratings or implicit (still numerical) feedbacks. However, the ever-growing availability of textual user reviews has become an important information resource, where a wealth of explicit product attributes/features and user attitudes/sentiments are expressed therein. This information rich resource of textual reviews have clearly exhibited brand-new approaches to solving many of the important problems that have been perplexing the research community for years, such as the paradox of cold-start, the explanation of recommendation, and the automatic generation of user or item profiles. However, it is only recently that the fundamental importance of textual reviews has gained wide recognition, perhaps mainly because of the difficulty in formatting, structuring and analyzing the free-texts. In this research, we stress the importance of incorporating textual reviews for recommendation through phrase-level sentiment analysis, and further investigate the role that the texts play in various important recommendation tasks.
What reviews are satisfactory: Novel features for automatic helpfulness voting
Authors: Yu Hong, Jun Lu, Jian-Min Yao, Qiaoming Zhu, Guodong Zhou
Venue: Conference SIGIR 2012
Year: 2012

This paper focuses on exploring the features of product reviews that satisfy users, by which to improve the automatic helpfulness voting for the reviews on commercial websites. Compared to the previous work, which single-mindedly adopts the textual features to assess the review helpfulness, we propose that user preferences are more explicit clues to infer the opinions of users on the review helpfulness. By using the user-preference based features, we firstly implement a binary helpfulness based review classification system to divide helpful reviews and useless, and on the basis, we secondly build a Ranking SVM based automatic helpfulness voting system (AHV) which rank reviews based on their helpfulness. Experiments used a large scale dataset containing over 34,266 reviews on 1289 products to test the systems, which achieves promising performances with accuracy of up to 0.72 and [email protected] of 0.25, and at least 9% accuracy improvement compared to the textual-feature based helpfulness assessment.
Cluster analysis for hypertext systems
Authors: Rodrigo A. Botafogo
Venue: Conference SIGIR 1993
Year: 1993

Identifying nodes of information that are highly related has many applications in any information systems, and in particular in hypertext systems. In this paper we present a technique to identify “natural” clusters in a hypertext. A natural cluster is a cluster that is not arbitrary, but depends only on intrinsic properties of the hypertext. In our case, the property we will use to identify the clusters is the number of independent paths between nodes. Using the graph theoretic definition of k-edge-components we present an aggregation technique to cluster the nodes. We then use this techniques to cluster three medium sized hypertexts that were developed by different authors for different users, using different methodologies. We also show how to use clustering to improve data display, browsing and retrieval.
People on drugs: Credibility of user statements in health communities
Authors: Subhabrata Mukherjee, Gerhard Weikum, Cristian Danescu-Niculescu-Mizil
Venue: Conference KDD 2014
Year: 2014

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.
Web-assisted annotation, semantic indexing and search of television and radio news
Authors: Mike Dowman, Valentin Tablan, Hamish Cunningham, Borislav Popov
Venue: Conference WWW 2005
Year: 2005

The Rich News system, that can automatically annotate radio and television news with the aid of resources retrieved from the World Wide Web, is described. Automatic speech recognition gives a temporally precise but conceptually inaccurate annotation model. Information extraction from related web news sites gives the opposite: conceptual accuracy but no temporal data. Our approach combines the two for temporally accurate conceptual semantic annotation of broadcast news. First low quality transcripts of the broadcasts are produced using speech recognition, and these are then automatically divided into sections corresponding to individual news stories. A key phrases extraction component finds key phrases for each story and uses these to search for web pages reporting the same event. The text and meta-data of the web pages is then used to create index documents for the stories in the original broadcasts, which are semantically annotated using the KIM knowledge management platform. A web interface then allows conceptual search and browsing of news stories, and playing of the parts of the media files corresponding to each news story. The use of material from the World Wide Web allows much higher quality textual descriptions and semantic annotations to be produced than would have been possible using the ASR transcript directly. The semantic annotations can form a part of the Semantic Web, and an evaluation shows that the system operates with high precision, and with a moderate level of recall.