OpenRaiser/Intern-Atlas huggingface dataset:
Learning routing queries in a query zone
Authors: Amit Singhal 0001, Mandar Mitra, Chris Buckley
Venue: Conference SIGIR 1997
Year: 1997

Word usage is domain dependent. A common word in one domain can be quite infrequent in another. In this study we exploit this property of word usage to improve document routing. We show that routing queries (profiles) learned only from the documents in a query domain are better than the routing profiles learned when query domains are not used. We approximate a query domain by a query zone. Experiments show that routing profiles learned from a query zone are 8--12% more effective than the profiles generated when no query zoning is used. 1 Background Document routing is an important problem in the field of information retrieval. [12] When a user has marked several articles as relevant to his/her information need, a system should be able to automatically learn the user&apos;s &quot;profile&quot; and should be able to route (send) new, potentially interesting, articles to the user. This problem has also been called as selective dissemination of information or information filtering. [4] Most current st...
Improving pseudo-relevance feedback in web information retrieval using web page segmentation
Authors: Shipeng Yu, Deng Cai, Ji-Rong Wen, Wei-Ying Ma
Venue: Conference WWW 2003
Year: 2003

In contrast to traditional document retrieval, a web page as a whole is not a good information unit to search because it often contains multiple topics and a lot of irrelevant information from navigation, decoration, and interaction part of the page. In this paper, we propose a VIsion-based Page Segmentation (VIPS) algorithm to detect the semantic content structure in a web page. Compared with simple DOM based segmentation method, our page segmentation scheme utilizes useful visual cues to obtain a better partition of a page at the semantic level. By using our VIPS algorithm to assist the selection of query expansion terms in pseudo-relevance feedback in web information retrieval, we achieve 27% performance improvement on Web Track dataset.
The impact of history length on personalized search
Authors: Yangbo Zhu, Jamie Callan, Jaime G. Carbonell
Venue: Conference SIGIR 2008
Year: 2008

Personalized search is a promising way to better serve different users' information needs. Search history is one of the major information sources for search personalization. We investigated the impact of history length on the effectiveness of personalized ranking. We carried out task-based user study for Web search, and obtained ranked relevance judgments for all queries. Query contexts derived from previous queries in the same task are used to re-rank results for the current query. Experimental results show that the performance of personalization generally improves as more queries are accumulated, but most of the benefits come from a few immediately preceding queries.
A dynamic cluster maintenance system for information retrieval
Authors: Fazli Can, Esen A. Ozkarahan
Venue: Conference SIGIR 1987
Year: 1987

Partitioning by clustering of very large databases is a necessity to reduce the space/time complexity of retrieval operations. However, the contemporary and modern retrieval environments demand dynamic maintenance of clusters. A new cluster maintenance strategy is proposed and its similarity/stability characteristics, cost analysis, and retrieval behavior in comparison with unclustered and completely reclustered database environments have been examined by means of a series of experiments.
ARSA: a sentiment-aware model for predicting sales performance using blogs
Authors: Yang Liu, Xiangji Huang, Aijun An, Xiaohui Yu
Venue: Conference SIGIR 2007
Year: 2007

Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.
Tag-oriented document summarization
Authors: Junyan Zhu, Can Wang, Xiaofei He, Jiajun Bu, Chun Chen, Shujie Shang, Mingcheng Qu, Gang Lu
Venue: Conference WWW 2009
Year: 2009

Social annotations on a Web document are highly generalized description of topics contained in that page. Their tagged frequency indicates the user attentions with various degrees. This makes annotations a good resource for summarizing multiple topics in a Web page. In this paper, we present a tag-oriented Web document summarization approach by using both document content and the tags annotated on that document. To improve summarization performance, a new tag ranking algorithm named EigenTag is proposed in this paper to reduce noise in tags. Meanwhile, association mining technique is employed to expand tag set to tackle the sparsity problem. Experimental results show our tag-oriented summarization has a significant improvement over those not using tags.
Timedtextrank: Adding the temporal dimension to multi-document summarization
Authors: Xiaojun Wan
Venue: Conference SIGIR 2007
Year: 2007

Graph-ranking based algorithms (e.g. TextRank) have been proposed for multi-document summarization in recent years. However, these algorithms miss an important dimension, the temporal dimension, for summarizing evolving topics. For an evolving topic, recent documents are usually more important than earlier documents because recent documents contain much more novel information than earlier documents and a novelty-oriented summary should be more appropriate to reflect the changing topic. We propose the TimedTextRank algorithm to make use of the temporal information of documents based on the graph-ranking based algorithm. A preliminary study is performed to demonstrate the effectiveness of the proposed TimedTextRank algorithm for dynamic multi-document summarization.
Exploiting social context for review quality prediction
Authors: Yue Lu, Panayiotis Tsaparas, Alexandros Ntoulas, Livia Polanyi
Venue: Conference WWW 2010
Year: 2010

Online reviews in which users publish detailed commentary about their experiences and opinions with products, services, or events are extremely valuable to users who rely on them to make informed decisions. However, reviews vary greatly in quality and are con-stantly increasing in number, therefore, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the problem by treating a review as a stand-alone docu-ment, extracting features from the review text, and learning a func-tion based on these features for predicting the review quality. In this work, we exploit contextual information about authors ’ iden-tities and social networks for improving review quality prediction. We propose a generic framework for incorporating social context information by adding regularization constraints to the text-based predictor. Our approach can effectively use the social context infor-mation available for large quantities of unlabeled reviews. It also has the advantage that the resulting predictor is usable even when social context is unavailable. We validate our framework within a real commerce portal and experimentally demonstrate that using social context information can help improve the accuracy of re-view quality prediction especially when the available training data is sparse.
Helping editors choose better seed sets for entity set expansion
Authors: Vishnu Vyas, Patrick Pantel, Eric Crestan
Venue: Conference CIKM 2009
Year: 2009

Sets of named entities are used heavily at commercial search engines such as Google, Yahoo and Bing. Acquiring sets of entities typically consists of combining semi-supervised expansion algorithms with manual cleaning of the resulting expanded sets. In this paper, we study the effects of different seed sets in a state-of-the-art semi-supervised expansion system and show a tremendous variation in expansion performance depending on the choice of seeds. We further show that human editors, in general, provide very bad seed sets, which perform well-below the average random seed set. We identify three factors of seed set composition, namely prototypicality, ambiguity and coverage, and we investigate their effects on expansion performance. Finally, we propose various automatic systems for improving editor-generated seed sets, which seek to remove ambiguous and other error-prone seed instances. An extensive experimental analysis shows that expansion quality, measured in R-precision, can be improved on average by a maximum of 46% by removing the right seeds from a seed set. Our automatic methods outperform the human editors seed sets and on average improve expansion performance by up to 34% over the original seed sets.
Using google distance to weight approximate ontology matches
Authors: Risto Gligorov, Warner ten Kate, Zharko Aleksovski, Frank van Harmelen
Venue: Conference WWW 2007
Year: 2007

Discovering mappings between concept hierarchies is widely regarded as one of the hardest and most urgent problems facing the Semantic Web. The problem is even harder in domains where concepts are inherently vague and ill-defined, and cannot be given a crisp definition. A notion of approximate concept mapping is required in such domains, but until now, no such notion is vailable.
Exploiting wikipedia as external knowledge for document clustering
Authors: Xiaohua Hu, Xiaodan Zhang, Caimei Lu, E. K. Park, Xiaohua Zhou
Venue: Conference KDD 2009
Year: 2009

In traditional text clustering methods, documents are represented as "bags of words" without considering the semantic information of each document. For instance, if two documents use different collections of core words to represent the same topic, they may be falsely assigned to different clusters due to the lack of shared core words, although the core words they use are probably synonyms or semantically associated in other forms. The most common way to solve this problem is to enrich document representation with the background knowledge in an ontology. There are two major issues for this approach: (1) the coverage of the ontology is limited, even for WordNet or Mesh, (2) using ontology terms as replacement or additional features may cause information loss, or introduce noise. In this paper, we present a novel text clustering method to address these two issues by enriching document representation with Wikipedia concept and category information. We develop two approaches, exact match and relatedness-match, to map text documents to Wikipedia concepts, and further to Wikipedia categories. Then the text documents are clustered based on a similarity metric which combines document content information, concept information as well as category information. The experimental results using the proposed clustering framework on three datasets (20-newsgroup, TDT2, and LA Times) show that clustering performance improves significantly by enriching document representation with Wikipedia concepts and categories.
A framework to predict the quality of answers with non-textual features
Authors: Jiwoon Jeon, W. Bruce Croft, Joon Ho Lee, Soyeon Park
Venue: Conference SIGIR 2006
Year: 2006

New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to predict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a significant improvement over our baseline.
Lower-bounding term frequency normalization
Authors: Yuanhua Lv, ChengXiang Zhai
Venue: Conference CIKM 2011
Year: 2011

In this paper, we reveal a common deficiency of the current retrieval models: the component of term frequency (TF) normalization by document length is not lower-bounded properly; as a result, very long documents tend to be overly penalized. In order to analytically diagnose this problem, we propose two desirable formal constraints to capture the heuristic of lower-bounding TF, and use constraint analysis to examine several representative retrieval functions. Analysis results show that all these retrieval functions can only satisfy the constraints for a certain range of parameter values and/or for a particular set of query terms. Empirical results further show that the retrieval performance tends to be poor when the parameter is out of the range or the query term is not in the particular set. To solve this common problem, we propose a general and efficient method to introduce a sufficiently large lower bound for TF normalization which can be shown analytically to fix or alleviate the problem. Our experimental results demonstrate that the proposed method, incurring almost no additional computational cost, can be applied to state-of-the-art retrieval functions, such as Okapi BM25, language models, and the divergence from randomness approach, to significantly improve the average precision, especially for verbose queries.
Combining coregularization and consensus-based self-training for multilingual text categorization
Authors: Massih-Reza Amini, Cyril Goutte, Nicolas Usunier
Venue: Conference SIGIR 2010
Year: 2010

We investigate the problem of learning document classifiers in a multilingual setting, from collections where labels are only partially available. We address this problem in the framework of multiview learning, where different languages correspond to different views of the same document, combined with semi-supervised learning in order to benefit from unlabeled documents. We rely on two techniques, coregularization and consensus-based self-training, that combine multiview and semi-supervised learning in different ways. Our approach trains different monolingual classifiers on each of the views, such that the classifiers' decisions over a set of unlabeled examples are in agreement as much as possible, and iteratively labels new examples from another unlabeled training set based on a consensus across language-specific classifiers. We derive a boosting-based training algorithm for this task, and analyze the impact of the number of views on the semi-supervised learning results on a multilingual extension of the Reuters RCV1/RCV2 corpus using five different languages. Our experiments show that coregularization and consensus-based self-training are complementary and that their combination is especially effective in the interesting and very common situation where there are few views (languages) and few labeled documents available.
A hybrid framework for text modeling with convolutional rnn
Authors: Chenglong Wang, Feijun Jiang, Hongxia Yang
Venue: Conference KDD 2017
Year: 2017

In this paper, we introduce a generic inference hybrid framework for Convolutional Recurrent Neural Network (conv-RNN) of semantic modeling of text, seamless integrating the merits on extracting different aspects of linguistic information from both convolutional and recurrent neural network structures and thus strengthening the semantic understanding power of the new framework. Besides, based on conv-RNN, we also propose a novel sentence classification model and an attention based answer selection model with strengthening power for the sentence matching and classification respectively. We validate the proposed models on a very wide variety of data sets, including two challenging tasks of answer selection (AS) and five benchmark datasets for sentence classification (SC). To the best of our knowledge, it is by far the most complete comparison results in both AS and SC. We empirically show superior performances of conv-RNN in these different challenging tasks and benchmark datasets and also summarize insights on the performances of other state-of-the-arts methodologies.
Relevance scores for triples from type-like relations
Authors: Hannah Bast, Björn Buchhold, Elmar Haussmann
Venue: Conference SIGIR 2015
Year: 2015

We compute and evaluate relevance scores for knowledge-base triples from type-like relations. Such a score measures the degree to which an entity "belongs" to a type. For example, Quentin Tarantino has various professions, including Film Director, Screenwriter, and Actor. The first two would get a high score in our setting, because those are his main professions. The third would get a low score, because he mostly had cameo appearances in his own movies. Such scores are essential in the ranking for entity queries, e.g. "American actors" or "Quentin Tarantino professions". These scores are different from scores for "correctness" or "accuracy" (all three professions above are correct and accurate). We propose a variety of algorithms to compute these scores. For our evaluation we designed a new benchmark, which includes a ground truth based on about 14K human judgments obtained via crowdsourcing. Inter-judge agreement is slightly over 90%. Existing approaches from the literature give results far from the optimum. Our best algorithms achieve an agreement of about 80% with the ground truth.
Summarizing highly structured documents for effective search interaction
Authors: Lanbo Zhang, Yi Zhang, Yunfei Chen
Venue: Conference SIGIR 2012
Year: 2012

As highly structured documents with rich metadata (such as products, movies, etc.) become increasingly prevalent, searching those documents has become an important IR problem. Unfortunately existing work on document summarization, especially in the context of search, has been mainly focused on unstructured documents, and little attention has been paid to highly structured documents. Due to the different characteristics of structured and unstructured documents, the ideal approaches for document summarization might be different. In this paper, we study the problem of summarizing highly structured documents in a search context. We propose a new summarization approach based on query-specific facet selection. Our approach aims to discover the important facets hidden behind a query using a machine learning approach, and summarizes retrieved documents based on those important facets. In addition, we propose to evaluate summarization approaches based on a utility function that measures how well the summaries assist users in interacting with the search results. Furthermore, we develop a game on Mechanical Turk to evaluate different summarization approaches. The experimental results show that the new summarization approach significantly outperforms two existing ones.
Affective Representations for Sarcasm Detection
Authors: Ameeta Agrawal, Aijun An
Venue: Conference SIGIR 2018
Year: 2018

Sarcasm detection from text has gained increasing attention. While one thread of research has emphasized the importance of affective content in sarcasm detection, another avenue of research has explored the effectiveness of word representations. In this paper, we introduce a novel model for automated sarcasm detection in text, called Affective Word Embeddings for Sarcasm (AWES), which incorporates affective information into word representations. Extensive evaluation on sarcasm detection on six datasets across three domains of text (tweets, reviews and forum posts) demonstrates the effectiveness of the proposed model. The experimental results indicate that while sentiment affective representations yield best results on datasets comprising of short length text such as tweets, richer representations derived from fine-grained emotions are more suitable for detecting sarcasm from longer length documents such as product reviews and discussion forum posts.
Leveraging transitions of emotions for sarcasm detection
Authors: Ameeta Agrawal, Aijun An, Manos Papagelis
Venue: Conference SIGIR 2020
Year: 2020

One popular thread of research in computational sarcasm detection involves modeling sarcasm as a contrast between positive and negative sentiment polarities or exploring more fine-grained categories of emotions such as happiness, sadness, surprise, and so on. Most current models, however, treat these affective features independently, without regard for the sequential information encoded among the affective states. In order to explore the role of transitions in affective states, we formulate the task of sarcasm detection as a sequence classification problem by leveraging the natural shifts in various emotions over the course of a piece of text. Experiments conducted on datasets from two different genres suggest that our proposed approach particularly benefits datasets with limited labeled data and longer instances of text.
Recoin: Relative Completeness in Wikidata
Authors: Vevake Balaraman, Simon Razniewski, Werner Nutt
Venue: Conference WWW 2018
Year: 2018

The collaborative knowledge base Wikidata is the central storage of Wikimedia projects, containing over 45 million data items. It acts as the hub for interlinking Wikipedia pages about a specific item in different languages, automates features such as infoboxes in Wikipedia, and is increasingly used for other applications such as data enrichment and question answering. Tracking the quality of Wikidata is an important issue for this project. In this paper we focus particularly on the completeness aspect. Several automated techniques have been adopted by Wikis to track and manage completeness, yet these techniques are generally subjective and do not provide a clear quality estimate at the level of entities. In this paper, we present an approach towards measuring Relative Completeness in Wikidata by comparison with data present for similar entities. This relative completeness approach is easily scalable with the introduction of new classes in the knowledge base, and has been implemented for all available entities in Wikidata. The results provide an intuition on the completeness of an entity comparing it with other similar entities. Here, we present our implementation approach along with a discussion on strategies and open challenges.
Faq retrieval using query-question similarity and bert-based query-answer relevance
Authors: Wataru Sakata, Tomohide Shibata, Ribeka Tanaka, Sadao Kurohashi
Venue: Conference SIGIR 2019
Year: 2019

Frequently Asked Question (FAQ) retrieval is an important task where the objective is to retrieve an appropriate Question-Answer (QA) pair from a database based on a user's query. We propose a FAQ retrieval system that considers the similarity between a user's query and a question as well as the relevance between the query and an answer. Although a common approach to FAQ retrieval is to construct labeled data for training, it takes annotation costs. Therefore, we use a traditional unsupervised information retrieval system to calculate the similarity between the query and question. On the other hand, the relevance between the query and answer can be learned by using QA pairs in a FAQ database. The recently-proposed BERT model is used for the relevance calculation. Since the number of QA pairs in FAQ page is not enough to train a model, we cope with this issue by leveraging FAQ sets that are similar to the one in question. We evaluate our approach on two datasets. The first one is localgovFAQ, a dataset we construct in a Japanese administrative municipality domain. The second is StackExchange dataset, which is the public dataset in English. We demonstrate that our proposed method outperforms baseline methods on these datasets.