OpenRaiser/Intern-Atlas huggingface dataset:
Automatic adaptation of Proper Noun Dictionaries through cooperation of machine learning and probabilistic methods
Authors: Georgios Petasis, Alessandro Cucchiarelli, Paola Velardi, Georgios Paliouras, Vangelis Karkaletsis, Constantine D. Spyropoulos
Venue: Conference SIGIR 2000
Year: 2000

The recognition of Proper Nouns (PNs) is considered an important task in the area of Information Retrieval and Extraction. However the high performance of most existing PN classifiers heavily depends upon the availability of large dictionaries of domain-specific Proper Nouns, and a certain amount of manual work for rule writing or manual tagging. Though it is not a heavy requirement to rely on some existing PN dictionary (often these resources are available on the web), its coverage of a domain corpus may be rather low, in absence of manual updating. In this paper we propose a technique for the automatic updating of an PN Dictionary through the cooperation of an inductive and a probabilistic classifier. In our experiments we show that, whenever an existing PN Dictionary allows the identification of 50% of the proper nouns within a corpus, our technique allows, without additional manual effort, the successful recognition of about 90% of the remaining 50%.
Two-stage query segmentation for information retrieval
Authors: Michael Bendersky, W. Bruce Croft, David A. Smith
Venue: Conference SIGIR 2009
Year: 2009

Modeling term dependence has been shown to have a significant positive impact on retrieval. Current models, however, use sequential term dependencies, leading to an increased query latency, especially for long queries. In this paper, we examine two query segmentation models that reduce the number of dependencies. We find that two-stage segmentation based on both query syntactic structure and external information sources such as query logs, attains retrieval performance comparable to the sequential dependence model, while achieving a 50% reduction in query latency.
The power of naive query segmentation
Authors: Matthias Hagen, Martin Potthast, Benno Stein, Christof Bräutigam
Venue: Conference SIGIR 2010
Year: 2010

We address the problem of query segmentation: given a keyword query submitted to a search engine, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve good segmentation performance on a gold standard but are fairly intricate. Our method is easy to implement and comes with a comparable accuracy.
Analyzing web text association to disambiguate abbreviation in queries
Authors: Xing Wei, Fuchun Peng, Benoît Dumoulin
Venue: Conference SIGIR 2008
Year: 2008

We introduce a statistical model for abbreviation disambiguation in Web search, based on analysis of Web data resources, including anchor text, click log and query log. By combining evidence from multiple sources, we are able to accurately disambiguate the abbreviation in queries. Experiments on real Web search queries show promising results.
Why we search: visualizing and predicting user behavior
Authors: Eytan Adar, Daniel S. Weld, Brian N. Bershad, Steven D. Gribble
Venue: Conference WWW 2007
Year: 2007

The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper, we take a first step at achieving this goal. We present a large scale study correlating the behaviors of Internet users on multiple systems ranging in size from 27 million queries to 14 million blog posts to 20,000 news articles. We formalize a model for events in these time-varying datasets and study their correlation. We have created an interface for analyzing the datasets, which includes a novel visual artifact, the DTWRadar, for summarizing differences between time series. Using our tool we identify a number of behavioral properties that allow us to understand the predictive power of patterns of use.
Content based social behavior prediction: a multi-task learning approach
Authors: Hongliang Fei, Ruoyi Jiang, Yuhao Yang, Bo Luo, Jun Huan
Venue: Conference CIKM 2011
Year: 2011

Information Flow Studies analyze the principles and mechanisms of social information distribution and is an essential research topic in social networks. Traditional approaches are primarily based on the social network graph topology. However, topology itself can not accurately reflect the user interests or activities. In this paper, we adopt a "microeconomics" approach to study social information diffusion and aim to answer the question that how social information flow and socialization behaviors are related to content similarity and user interests. In particular, we study content-based social activity prediction, i.e., to predict a user's response (e.g. comment or like) to their friends' postings (e.g. blogs) w.r.t. message content. In our solution, we cast the social behavior prediction problem as a multi-task learning problem, in which each task corresponds to a user. We have designed a novel multi-task learning algorithm that is specifically designed for learning information flow in social networks. In our model, we apply l1 and Tikhonov regularization to obtain a sparse and smooth model in a linear multi-task learning framework. Using comprehensive experimental study, we have demonstrated the effectiveness of the proposed learning method.
An IR-based Evaluation Framework for Web Search Query Segmentation
Authors: Rishiraj Saha Roy, Niloy Ganguly, Monojit Choudhury, Srivatsan Laxman
Venue: Conference SIGIR 2012
Year: 2012

This paper presents the first evaluation framework for Web search query segmentation based directly on IR performance. In the past, segmentation strategies were mainly validated against manual annotations. Our work shows that the goodness of a segmentation algorithm as judged through evaluation against a handful of human annotated segmentations hardly reflects its effectiveness in an IR-based setup. In fact, state-of the-art algorithms are shown to perform as good as, and sometimes even better than human annotations -- a fact masked by previous validations. The proposed framework also provides us an objective understanding of the gap between the present best and the best possible segmentation algorithm. We draw these conclusions based on an extensive evaluation of six segmentation strategies, including three most recent algorithms, vis-a-vis segmentations from three human annotators. The evaluation framework also gives insights about which segments should be necessarily detected by an algorithm for achieving the best retrieval results. The meticulously constructed dataset used in our experiments has been made public for use by the research community.
Semantic convolution kernels over dependency trees: smoothed partial tree kernel
Authors: Danilo Croce, Alessandro Moschitti, Roberto Basili
Venue: Conference CIKM 2011
Year: 2011

In recent years, natural language processing techniques have been used more and more in IR. Among other syntactic and semantic parsing are effective methods for the design of complex applications like for example question answering and sentiment analysis. Unfortunately, extracting feature representations suitable for machine learning algorithms from linguistic structures is typically difficult. In this paper, we describe one of the most advanced piece of technology for automatic engineering of syntactic and semantic patterns. This method merges together convolution dependency tree kernels with lexical similarities. It can efficiently and effectively measure the similarity between dependency structures, whose lexical nodes are in part or completely different. Its use in powerful algorithm such as Support Vector Machines (SVMs) allows for fast design of accurate automatic systems.
Lights, camera, action: Knowledge extraction from movie scripts
Authors: Niket Tandon, Gerhard Weikum, Gerard de Melo, Abir De
Venue: Conference WWW 2015
Year: 2015

With the success of large knowledge graphs, research on automatically acquiring commonsense knowledge is revived. One kind of knowledge that has not received attention is that of human activities. This paper presents an information extraction pipeline for systematically distilling activity knowledge from a corpus of movie scripts. Our semantic frames capture activities together with their participating agents and their typical spatial, temporal and sequential contexts. The resulting knowledge base comprises about 250,000 activities with links to specific movie scenes where they occur.
Topic- STG: Extending the session-based temporal graph approach for personalized tweet recommendation
Authors: Jianjun Yu, Yi Shen, Zhenglu Yang
Venue: Conference WWW 2014
Year: 2014

Micro-blogging is experiencing fantastic success in the worldwide. However, during its rapid development, it has encountered the problem of information overload, which has troubled many users. In this paper, we mainly focus on the task of tweet recommendation to address this problem. We extend the session-based temporal graph (STG) approach as Topic-STG for tweet recommendation which comprehensively considers three types of features in Twitter: the textual information, the time factor, and the users' behavior. The experimental results conducted on a real dataset demonstrate the effectiveness of our approach.
An eye-tracking study of query reformulation
Authors: Carsten Eickhoff, Sebastian Dungs, Vu Tran
Venue: Conference SIGIR 2015
Year: 2015

Information about a user's domain knowledge and interest can be important signals for many information retrieval tasks such as query suggestion or result ranking. State-of-the-art user models rely on coarse-grained representations of the user's previous knowledge about a topic or domain. In this paper, we study query refinement using eye-tracking in order to gain precise and detailed insight into which terms the user was exposed to in a search session and which ones they showed a particular interest in. We measure fixations on the term level, allowing for a detailed model of user attention. To allow for a wide-spread exploitation of our findings, we generalize from the restrictive eye-gaze tracking to using more accessible signals: mouse cursor traces. Based on the public API of a popular search engine, we demonstrate how query suggestion candidates can be ranked according to traces of user attention and interest, resulting in significantly better performance than achieved by an attention-oblivious industry solution. Our experiments suggest that modelling term-level user attention can be achieved with great reliability and holds significant potential for supporting a range of traditional IR tasks.
Wdaqua-core1: a question answering service for rdf knowledge bases
Authors: Dennis Diefenbach, Kamal Deep Singh, Pierre Maret
Venue: Conference WWW 2018
Year: 2018

In the last two decades a new part of the web grew significantly, namely the Semantic Web. It contains many Knowledge Bases (KB) about different areas like music, books, publications, live science and many more. Question Answering (QA) over KBs is seen as the most promising approach to bring this data to end-users. We describe WDAqua-core1, a QA service for querying RDF knowledge-bases. It is multilingual, it supports different RDF knowledge bases and it understands both full natural language questions and keyword questions.
Robust group linkage
Authors: Pei Li, Xin Luna Dong, Songtao Guo, Andrea Maurino, Divesh Srivastava
Venue: Conference WWW 2015
Year: 2015

We study the problem of group linkage: linking records that refer to multiple entities in the same group. Applications for group linkage include finding businesses in the same chain, finding social network users from the same organization, and so on. Group linkage faces new challenges compared to traditional entity resolution. First, although different members in the same group can share some similar global values of an attribute, they represent different entities so can also have distinct local values for the same or different attributes, requiring a high tolerance for value diversity. Second, we need to be able to distinguish local values from erroneous values. We present a robust two-stage algorithm: the first stage identifies pivots--maximal sets of records that are very likely to belong to the same group, while being robust to possible erroneous values; the second stage collects strong evidence from the pivots and leverages it for merging more records into the same group, while being tolerant to differences in local values of an attribute. Experimental results show the high effectiveness and efficiency of our algorithm on various real-world data sets.
Building re-usable dictionary repositories for real-world text mining
Authors: Shantanu Godbole, Indrajit Bhattacharya, Ajay Gupta, Ashish Verma
Venue: Conference CIKM 2010
Year: 2010

Text mining, though still a nascent industry, has been growing quickly along with the awareness of the importance of unstructured data in business analytics, customer retention and extension, social media, and legal applications. There has been a recent increase in the number of commercial text mining product and service offerings, but successful or wide-spread deployments are rare, mainly due to a dependence on the expertise and skill of practitioners. Accordingly, there is a growing need for re-usable repositories for text mining. In this paper, we focus on dictionary-based text mining and its role in enabling practitioners in understanding and analyzing large text datasets. We motivate and define the problem of exploratory dictionary construction for capturing concepts of interest, and propose a framework for efficient construction, tuning, and re-use of these dictionaries across datasets. The construction framework offers a range of interaction modes to the user to quickly build concept dictionaries over large datasets. We also show how to adapt one or more dictionaries across domains and tasks, thereby enabling reuse of knowledge and effort in industrial practice. We present results and case studies on real-life CRM analytics datasets, where such repositories and tooling significantly cut down practitioner time and effort for dictionary-based text mining.
Fusion in information retrieval: Sigir 2018 half-day tutorial
Authors: Oren Kurland, J. Shane Culpepper
Venue: Conference SIGIR 2018
Year: 2018

Fusion is an important and central concept in Information Retrieval. The goal of fusion methods is to merge different sources of information so as to address a retrieval task. For example, in the adhoc retrieval setting, fusion methods have been applied to merge multiple document lists retrieved for a query. The lists could be retrieved using different query representations, document representations, ranking functions and corpora. The goal of this half day, intermediate-level, tutorial is to provide a methodological view of the theoretical foundations of fusion approaches, the numerous fusion methods that have been devised and a variety of applications for which fusion techniques have been applied.
Tracy: Tracing facts over knowledge graphs and text
Authors: Mohamed H. Gad-Elrab, Daria Stepanova, Jacopo Urbani, Gerhard Weikum
Venue: Conference WWW 2019
Year: 2019

In order to accurately populate and curate Knowledge Graphs (KGs), it is important to distinguish ?s?p?o? facts that can be traced back to sources from facts that cannot be verified. Manually validating each fact is time-consuming. Prior work on automating this task relied on numerical confidence scores which might not be easily interpreted. To overcome this limitation, we present Tracy, a novel tool that generates human-comprehensible explanations for candidate facts. Our tool relies on background knowledge in the form of rules to rewrite the fact in question into other easier-to-spot facts. These rewritings are then used to reason over the candidate fact creating semantic traces that can aid KG curators. The goal of our demonstration is to illustrate the main features of our system and to show how the semantic traces can be computed over both text and knowledge graphs with a simple and intuitive user interface.
A segment-based hidden markov model for real-setting pinyin-to-chinese conversion
Authors: Xiaohua Zhou, Xiaohua Hu, Xiaodan Zhang, Xiajiong Shen
Venue: Conference CIKM 2007
Year: 2007

Hidden markov model (HMM) is frequently used for Pinyin-to-Chinese conversion. But it only captures the dependency with the preceding character. Higher order markov models can bring higher accuracy, but are computationally unaffordable to average PC settings. We propose a segment-based hidden markov model (SHMM), which has the same magnitude of complexity as first-order HMM, but generates higher decoding accuracy. SHMM tells a word from a bigram connecting two words, and assigns a reasonable probability to words as a whole. It is more powerful than HMM to decode words containing over two characters. We conduct a comprehensive Pinyin-to-Chinese conversion evaluation on Lancaster corpus. The experiment shows the perfect sentence accuracy is improved from 34.7% (HMM) to 43.3% (SHMM). The one-error sentence accuracy is increased from 72.7% to 78.3%. Furthermore, SHMM can seamlessly integrate with pinyin typing correction, acronym pinyin input, user-defined words, and self-adaptive learning all of which are a must for a commercial Pinyin-to-Chinese conversion product in order to improve the efficiency of pinyin input.
Context-to-session matching: Utilizing whole session for response selection in information-seeking dialogue systems
Authors: Zhenxin Fu, Shaobo Cui 0001, Mingyue Shang, Feng Ji, Dongyan Zhao 0001, Haiqing Chen, Rui Yan 0001
Venue: Conference KDD 2020
Year: 2020

We study the retrieval-based multi-turn information-seeking dialogue systems, which are widely used in many scenarios. Most of the previous works select the response according to the matching degree between the query's context and the candidate responses. Though great progress has been made, existing works ignore the contexts of the responses, which could provide rich information for selecting the most appropriate response. The more similar the query's context and certain response's context are, the more likely they are to indicate the same question, and thus, the more likely this response is to answer the query. In this paper, we consider the response and its context as a whole session and explore the task of matching the query's context with the sessions. More specifically, we propose to match between the query's context and response's context and integrate the context-to-context matching with context-to-response matching. Experiment results prove that our proposed context-to-session method outperforms the strong baselines significantly.
Layout and semantics: Combining representations for mathematical formula search
Authors: Kenny Davila, Richard Zanibbi
Venue: Conference SIGIR 2017
Year: 2017

Math-aware search engines need to support formulae in queries. Mathematical expressions are typically represented as trees defining their operational semantics or visual layout. We propose searching both formula representations using a three-layer model. The first layer selects candidates using spectral matching over tree node pairs. The second layer aligns a query with candidates and computes similarity scores based on structural matching. In the third layer, similarity scores are combined using linear regression. The two representations are combined using retrieval in parallel indices and regression over similarity scores. For NTCIR-12 Wikipedia Formula Browsing task relevance rankings, we see each layer increasing ranking quality and improved results when combining representations as measured by Bpref and nDCG scores.
Legal intelligence: Algorithmic, data, and social challenges
Authors: Changlong Sun, Yating Zhang, Xiaozhong Liu 0001, Fei Wu 0001
Venue: Conference SIGIR 2020
Year: 2020

In the digital era, information retrieval, text/knowledge mining, and NLP techniques are playing increasingly vital roles in legal domain. While the open datasets and innovative deep learning methodologies provide critical potentials, in the legal-domain, efforts need to be made to transfer the theoretical/algorithmic models into the real applications to assist users, lawyers, judges and the legal professions to solve the real problems. The objective of this workshop is to aggregate studies/applications of text mining/retrieval and NLP automation in the context of classical/novel legal tasks, which address algorithmic, data and social challenges of legal intelligence. Keynote and invited presentations from industry and academic will be able to fill the gap between ambition and execution in the legal domain.
Successful new-entry prediction for multi-party online conversations via latent topics and discourse modeling
Authors: Lingzhi Wang, Jing Li, Xingshan Zeng, Kam-Fai Wong
Venue: Conference WWW 2022
Year: 2022

With the increasing popularity of social media, online interpersonal communication now plays an essential role in people’s everyday information exchange. Whether and how a newcomer can better engage in the community has attracted great interest due to its application in many scenarios. Although some prior works that explore early socialization have obtained salient achievements, they are focusing on sociological surveys based on the small group. To help individuals get through the early socialization period and engage well in online conversations, we study a novel task to foresee whether a newcomer’s message will be responded to by other participants in a multi-party conversation (henceforth Successful New-entry Prediction)1. The task would be an important part of the research in online assistants and social media. To further investigate the key factors indicating such engagement success, we employ an unsupervised neural network, Variational Auto-Encoder (VAE), to examine the topic content and discourse behavior from newcomer’s chatting history and conversation’s ongoing context. Furthermore, two large-scale datasets, from Reddit and Twitter, are collected to support further research on new-entries. Extensive experiments on both Twitter and Reddit datasets show that our model significantly outperforms all the baselines and popular neural models. Additional explainable and visual analyses on new-entry behavior shed light on how to better join in others’ discussions.
Ranking enhanced dialogue generation
Authors: Changying Hao, Liang Pang, Yanyan Lan, Fei Sun, Jiafeng Guo, Xueqi Cheng
Venue: Conference CIKM 2020
Year: 2020

How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., recurrent neural networks, attention mechanisms, and hierarchical structures) to model the history. However, a recent empirical study by Sankar et al. has shown that these architectures lack the ability of understanding and modeling the dynamics of the dialogue history. For example, the widely used architectures are insensitive to perturbations of the dialogue history, such as words shuffling, utterances missing, and utterances reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper. Despite the traditional representation encoder and response generation modules, an additional ranking module is introduced to model the ranking relation between the former utterance and consecutive utterances. Specifically, the former utterance and consecutive utterances are treated as query and corresponding documents, and both local and global ranking losses are designed in the learning process. In this way, the dynamics in the dialogue history can be explicitly captured. To evaluate our proposed models, we conduct extensive experiments on three public datasets, i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models produce better responses in terms of both quantitative measures and human judgments, as compared with the state-of-the-art dialogue generation models. Furthermore, we give some detailed experimental analysis to show where and how the improvements come from.