OpenRaiser/Intern-Atlas huggingface dataset:
An encoder-memory-decoder framework for sub-event detection in social media
Authors: Guandan Chen, Nan Xu, Wenji Mao
Venue: Conference CIKM 2018
Year: 2018

Sub-event detection can help faster and deeper understanding of an event by providing human-friendly clusters, and thus has become an important research topic in Web mining and knowledge management. In existing sub-event detection methods, clustering based methods are brittle for using heuristic similarity metric to judge whether documents belong to the same sub-event, while topic model based methods are limited to the bag of words assumption. To overcome these drawbacks in previous research, in this paper, we propose an encoder-memory-decoder framework for sub-event detection. Our model learns document and sub-event representations suitable for the similarity metric in a data-driven manner, and transforms sub-event detection into selecting the most proper sub-event representation that can maximize text reconstruction probability. Considering the case of over-fitting, we also apply transfer learning in our model. To the best of our knowledge, our model is the first to develop an unsupervised deep neural model for sub-event detection. We use Twitter as an examplar social media platform for our study, and experimental results show that our model outperforms baseline methods for sub-event detection.
Word vector compositionality based relevance feedback using kernel density estimation
Authors: Dwaipayan Roy, Debasis Ganguly, Mandar Mitra, Gareth J. F. Jones
Venue: Conference CIKM 2016
Year: 2016

A limitation of standard information retrieval (IR) models is that the notion of term composionality is restricted to pre-defined phrases and term proximity. Standard text based IR models provide no easy way of representing semantic relations between terms that are not necessarily phrases, such as the equivalence relationship between `osteoporosis' and the terms `bone' and `decay'. To alleviate this limitation, we introduce a relevance feedback (RF) method which makes use of word embedded vectors. We leverage the fact that the vector addition of word embeddings leads to a semantic composition of the corresponding terms, e.g. addition of the vectors for `bone' and `decay' yields a vector that is likely to be close to the vector for the word `osteoporosis'. Our proposed RF model enables incorporation of semantic relations by exploiting term compositionality with embedded word vectors. We develop our model for RF as a generalization of the relevance model (RLM). Our experiments demonstrate that our word embedding based RF model significantly outperforms the RLM model on standard TREC test collections, namely the TREC 6,7,8 and Robust ad-hoc and the TREC 9 and 10 WT10G test collections.
Automatic Task Completion Flows from Web APIs
Authors: Kyle Williams, Seyyed Hadi Hashemi, Imed Zitouni
Venue: Conference SIGIR 2019
Year: 2019

The Web contains many APIs that could be combined in countless ways to enable Intelligent Assistants to complete all sorts of tasks. We propose a method to automatically produce task completion flows from a collection of these APIs by combining them in a graph and automatically extracting paths from the graph for task completion. These paths chain together API calls and use the output of executed APIs as inputs to others. We automatically extract these paths from an API graph in response to a user query and then rank the paths by the likelihood of them leading to user satisfaction. We apply our approach for task completion in the email and calendar domains and show how it can be used to automatically create task completion flows.
Continuous word embeddings for detecting local text reuses at the semantic level
Authors: Qi Zhang, Jihua Kang, Jin Qian, Xuanjing Huang
Venue: Conference SIGIR 2014
Year: 2014

Text reuse is a common phenomenon in a variety of user-generated content. Along with the quick expansion of social media, reuses of local text are occurring much more frequently than ever before. The task of detecting these local reuses serves as an essential step for many applications. It has attracted extensive attention in recent years. However, semantic level similarities have not received consideration in most previous works. In this paper, we introduce a novel method to efficiently detect local reuses at the semantic level for large scale problems. We propose to use continuous vector representations of words to capture the semantic level similarities between short text segments. In order to handle tens of billions of documents, methods based on information geometry and hashing methods are introduced to aggregate and map text segments presented by word embeddings to binary hash codes. Experimental results demonstrate that the proposed methods achieve significantly better performance than state-of-the-art approaches in all six document collections belonging to four different categories. At some recall levels, the precisions of the proposed method are even 10 times higher than previous methods. Moreover, the efficiency of the proposed method is comparable to or better than that of some other hashing methods.
Visualization of search results: a comparative evaluation of text, 2d, and 3d interfaces
Authors: Marc M. Sebrechts, John Cugini, Sharon J. Laskowski, Joanna Vasilakis, Michael S. Miller
Venue: Conference SIGIR 1999
Year: 1999

Although there have been many prototypes of visualization in support of information retrieval, there has been little systematic evaluation that distinguishes the benefits of the visualization per se from that of various accompanying features. The current study focuses on such an evaluation of NIRVE, a tool that supports visualization of search results. Insofar as possible, functionally equivalent 3D, 2D, and text versions of NIRVE were implemented. Nine novices and six professional users completed a series of information-seeking tasks on a set of retrieved documents. There were high interface costs for the 3D visualization, although those costs decreased substantially with experience. Performance was best when the tool's properties matched task demands; only under the right combination of task, user, and interface did 3D visualization result in performance comparable to functionally matched 2D and textual tools.
Open-schema event profiling for massive news corpora
Authors: Quan Yuan, Xiang Ren, Wenqi He, Chao Zhang, Xinhe Geng, Lifu Huang, Heng Ji, Chin-Yew Lin, Jiawei Han
Venue: Conference CIKM 2018
Year: 2018

With the rapid growth of online information services, a sheer volume of news data becomes available. To help people quickly digest the explosive information, we define a new problem - schema-based news event profiling - profiling events reported in open-domain news corpora, with a set of slots and slot-value pairs for each event, where the set of slots forms the schema of an event type. Such profiling not only provides readers with concise views of events, but also facilitates various applications such as information retrieval, knowledge graph construction and question answering. It is however a quite challenging task. The first challenge is to find out events and event types because they are both initially unknown. The second difficulty is the lack of pre-defined event-type schemas. Lastly, even with the schemas extracted, to generate event profiles from them is still essential yet demanding. To address these challenges, we propose a fully automatic, unsupervised, three-step framework to obtain event profiles. First, we develop a Bayesian non-parametric model to detect events and event types by exploiting the slot expressions of the entities mentioned in news articles. Second, we propose an unsupervised embedding model for schema induction that encodes the insight: an entity may serve as the values of multiple slots in an event, but if it appears in more sentences along with the same set of more entities in the event, its slots in these sentences tend to be similar. Finally, we build event profiles by extracting slot values for each event based on the slots' expression patterns. To the best of our knowledge, this is the first work on schema-based profiling for news events. Experimental results on a large news corpus demonstrate the superior performance of our method against the state-of-the-art baselines on event detection, schema induction and event profiling.
WIRE: an automated report generation system using topical and temporal summarization
Authors: Yunseok Noh, Yongmin Shin, Junmo Park, A.-Yeong Kim, Su-Jeong Choi, Hyun-Je Song, Seong-Bae Park, Se-Young Park
Venue: Conference SIGIR 2020
Year: 2020

The demand for a tool for summarizing emerging topics is increasing in modern life since the tool can deliver well-organized information to its users. Even though there are already a number of successful search systems, the system which automatically summarizes and organizes the content of emerging topics is still in its infancy. To fulfill such demand, we introduce an automated report generation system that generates a well-summarized human-readable report for emerging topics. In this report generation system, emerging topics are automatically discovered by a topic model and news articles are indexed by the discovered topics. Then, a topical summary and a timeline summary for each topic is generated by a topical multi-document summarizer and a timeline summarizer respectively. In order to enhance the apprehensibility of the users, the proposed report system provides two report modes. One is Today's Briefing which summarizes five discovered topics of every day, and the other is Full Report which shows a long-term view of each topic with a detailed topical summary and an important event timeline.
Measuring and controlling text generation by semantic search
Authors: Jieh-Sheng Lee
Venue: Conference WWW 2020
Year: 2020

Our motivation in this work is to measure patent text generation by semantic search, particularly by textual similarity in high dimensional space for neural network models. The objective is to control patent text generation by semantic search. Conceptually it is an attempt to integrate two subfields in NLP: text generation and semantic search. In our previous milestone of the PatentTransformer project, a prototype based on GPT-2 is capable of generating fluent patent title, abstract, independent claim, and dependent claim. However, beneath the surface form, the quality issue in the generated patent text was less explored. How to control text generation is also a hard problem in NLP field. We would like to address these issues in this work and experiment with different approaches. On the measurement side, this work will address the quality measurement issue from the perspective of textual similarity. Based on that, the approaches we propose include two embedding spaces, span-based textual similarity, and language model for patent claim spans. One the control side, we propose a knob-turning approach for controlling text generation based on measuring a range of textual similarity. In this way, we can search for a Goldilocks zone in which the similarity of generated patent text is close to but not too far from prior patents. We hypothesize that patent novelty may exist in such a zone.
P3 ranker: Mitigating the gaps between pre-training and ranking fine-tuning with prompt-based learning and pre-finetuning
Authors: Xiaomeng Hu, Shi Yu, Chenyan Xiong, Zhenghao Liu, Zhiyuan Liu, Ge Yu
Venue: Conference SIGIR 2022
Year: 2022

Compared to other language tasks, applying pre-trained language models (PLMs) for search ranking often requires more nuances and training signals. In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task knowledge gap considering the discrepancy between the knowledge needed in ranking and that learned during pre-training. To mitigate these gaps, we propose Pre-trained, Prompt-learned and Pre-finetuned Neural Ranker (P3 Ranker). P3 Ranker leverages prompt-based learning to convert the ranking task into a pre-training like schema and uses pre-finetuning to initialize the model on intermediate supervised tasks. Experiments on MS MARCO and Robust04 show the superior performances of P3 Ranker in few-shot ranking. Analyses reveal that P3 Ranker is able to better accustom to the ranking task through prompt-based learning and retrieve necessary ranking-oriented knowledge gleaned in pre-finetuning, resulting in data-efficient PLM adaptation. Our code is available at https://github.com/NEUIR/P3Ranker.
Multi-graph based multi-scenario recommendation in large-scale online video services
Authors: Fan Zhang, Qiuying Peng, Yulin Wu, Zheng Pan, Rong Zeng, Da Lin, Yue Qi
Venue: Conference WWW 2022
Year: 2022

Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods. However, they still face de-biasing challenges such as exposure bias and cold-start problem, where circulations of machine learning training on human interaction history leads algorithms to repeatedly suggest exposed items while ignoring less-active ones. Additional problems exist in multi-scenario platforms, e.g. appropriate data fusion from subsidiary scenarios, which we observe could be alleviated through graph structured data integration via message passing. In this paper, we present a multi-graph structured multi-scenario recommendation solution, which encapsulates interaction data across scenarios with multi-graph and obtains representation via graph learning. Extensive offline and online experiments on real-world datasets are conducted where the proposed method demonstrates an increase of 0.63% and 0.71% in CTR and Video Views per capita on new users over deployed set of baselines and outperforms regular method in increasing the number of outer-scenario videos by 25% and video watches by 116%, validating its superiority in activating cold videos and enriching target recommendation.
Quam: Adaptive retrieval through query affinity modelling
Authors: Mandeep Rathee, Sean MacAvaney, Avishek Anand
Venue: Conference WSDM 2025
Year: 2025

A central task in information retrieval and the NLP communities is relevance modeling, which aims to rank documents based on their expressed information needs Many knowledge-intensive retrieval tasks are powered by a first-stage retrieval stage for context selection, followed by a more involved task-specific model. However, using this filtering (cascading) approach inherently limits the recall of subsequent stages. Recently, adaptive re-ranking techniques have been proposed to overcome this issue by continually selecting documents from the whole corpus, rather than only considering an initial pool of documents. However, so far these approaches have been limited to heuristic design choices, particularly in terms of the criteria for document selection. In this work, we propose a unifying view of the nascent area of adaptive retrieval by proposing Quam, a query-affinity model of adaptive re-ranking that includes two complementary components: (1) a more principled algorithm for document selection, and (2) a data-driven approach to model document co-relevance during indexing. Our extensive experimental evidence shows that our proposed approach improves the recall performance by up to 26% over the standard re-ranking baselines. Further, the query affinity modelling and relevance-aware document graph components can be injected into any adaptive retrieval approach. The experimental results show the existing adaptive retrieval approach improves recall by up to 12%.
Revisiting online personal search metrics with the user in mind
Authors: Azin Ashkan, Donald Metzler
Venue: Conference SIGIR 2019
Year: 2019

Traditional online quality metrics are based on search and browsing signals, such as position and time of the click. Such metrics typically model all users' behavior in exactly the same manner. Modeling individuals' behavior in Web search may be challenging as the user's historical behavior may not always be available (e.g., if the user is not signed into a given service). However, in personal search, individual users issue queries over their personal corpus (e.g. emails, files, etc.) while they are logged into the service. This brings an opportunity to calibrate online quality metrics with respect to an individual's search habits. With this goal in mind, the current paper focuses on a user-centric evaluation framework for personal search by taking into account variability of search and browsing behavior across individuals. The main idea is to calibrate each interaction of a user with respect to their historical behavior and search habits. To formalize this, a characterization of online metrics is proposed according to the relevance signal of interest and how the signal contributes to the computation of the gain in a metric. The proposed framework introduces a variant of online metrics called pMetrics (short for personalized metrics) that are based on the average search habits of users for the relevance signal of interest. Through extensive online experiments on a large population of GMail search users, we show that pMetrics are effective in terms of their sensitivity, robustness, and stability compared to their standard variants as well as baselines with different normalization factors.
Separating appearance from deformation
Authors: Nebojsa Jojic, Patrice Y. Simard, Brendan J. Frey, David Heckerman
Venue: Conference ICCV 2001
Year: 2001

By representing images and image prototypes by linear subspaces spanned by "tangent vectors" (derivatives of an image with respect to translation, rotation, etc.), impressive invariance to known types of uniform distortion can be built into feedforward discriminators. We describe a new probability model that can jointly cluster data and learn mixtures of nonuniform, smooth deformation fields. Our fields are based on low-frequency wavelets, so they use very few parameters to model a wide range of smooth deformations (unlike, e.g., factor analysis, which uses a large number of parameters to model deformations). In spirit, our ideas are most similar to the idea of separating content from style published by Tenenbaum and Freeman. However, our models do not need labeled data for training, and thus allow for unsupervised separation of appearance from deformation. We give results on handwritten digit recognition and face recognition.
Articulated body posture estimation from multi-camera voxel data
Authors: Ivana Mikic, Mohan M. Trivedi, Edward Hunter, Pamela C. Cosman
Venue: Conference CVPR 2001
Year: 2001

We present a framework for articulated body model acquisition and tracking from voxel data. A 3D voxel reconstruction of the person's body is computed from silhouettes extracted from four cameras. The model acquisition process is fully automated. In the first frame, body parts are located sequentially. The head is located first, since its shape and size are unique and stable. Other parts are found by sequential template growing and fitting. This initial estimate of body part locations, sizes and orientations is then used as a measurement for the extended Kalman filter which ensures a valid articulated body model. The same filter, with a slightly modified state and state transition matrix, is then used for tracking. The performance of the system has been evaluated on several video sequences with promising results.
The one-shot similarity kernel
Authors: Lior Wolf, Tal Hassner, Yaniv Taigman
Venue: Conference ICCV 2009
Year: 2009

The One-Shot similarity measure has recently been introduced in the context of face recognition where it was used to produce state-of-the-art results. Given two vectors, their One-Shot similarity score reflects the likelihood of each vector belonging in the same class as the other vector and not in a class defined by a fixed set of “negative” examples. The potential of this approach has thus far been largely unexplored. In this paper we analyze the One-Shot score and show that: (1) when using a version of LDA as the underlying classifier, this score is a Conditionally Positive Definite kernel and may be used within kernel-methods (e.g., SVM), (2) it can be efficiently computed, and (3) that it is effective as an underlying mechanism for image representation. We further demonstrate the effectiveness of the One-Shot similarity score in a number of applications including multiclass identification and descriptor generation.
Structure preserving embedding
Authors: Blake Shaw, Tony Jebara
Venue: Conference ICML 2009
Year: 2009

Structure Preserving Embedding (SPE) is an algorithm for embedding graphs in Euclidean space such that the embedding is low-dimensional and preserves the global topological properties of the input graph. Topology is preserved if a connectivity algorithm, such as k-nearest neighbors, can easily recover the edges of the input graph from only the coordinates of the nodes after embedding. SPE is formulated as a semidefinite program that learns a low-rank kernel matrix constrained by a set of linear inequalities which captures the connectivity structure of the input graph. Traditional graph embedding algorithms do not preserve structure according to our definition, and thus the resulting visualizations can be misleading or less informative. SPE provides significant improvements in terms of visualization and lossless compression of graphs, outperforming popular methods such as spectral embedding and Laplacian eigen-maps. We find that many classical graphs and networks can be properly embedded using only a few dimensions. Furthermore, introducing structure preserving constraints into dimensionality reduction algorithms produces more accurate representations of high-dimensional data.
Yes we can simplex volume maximization for descriptive web–scale matrix factorization
Authors: Christian Thurau, Kristian Kersting, Christian Bauckhage
Venue: Conference CIKM 2010
Year: 2010

Matrix factorization methods are among the most common techniques for detecting latent components in data. Popular examples include the Singular Value Decomposition or Non-negative Matrix Factorization. Unfortunately, most methods suffer from high computational complexity and therefore do not scale to massive data. In this paper, we present a linear time algorithm for the factorization of gigantic matrices that iteratively yields latent components. We consider a constrained matrix factorization s.t.~the latent components form a simplex that encloses most of the remaining data. The algorithm maximizes the volume of that simplex and thereby reduces the displacement of data from the space spanned by the latent components. Hence, it also lowers the Frobenius norm, a common criterion for matrix factorization quality. Our algorithm is efficient, well-grounded in distance geometry, and easily applicable to matrices with billions of entries. In addition, the resulting factors allow for an intuitive interpretation of data: every data point can now be expressed as a convex combination of the most extreme and thereby often most descriptive instances in a collection of data. Extensive experimental validations on web-scale data, including 80 million images and 1.5 million twitter tweets, demonstrate superior performance compared to related factorization or clustering techniques.
Compact approximations to bayesian predictive distributions
Authors: Edward Lloyd Snelson, Zoubin Ghahramani
Venue: Conference ICML 2005
Year: 2005

We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing the KL divergence between the true predictive density and a suitable compact approximation. We consider various methods for doing this, both sampling based approximations, and deterministic approximations such as expectation propagation. These methods are tested on a mixture of Gaussians model for density estimation and on binary linear classification, with both synthetic data sets for visualization and several real data sets. Our results show significant reductions in prediction time and memory footprint.