OpenRaiser/Intern-Atlas huggingface dataset:
Web scale nlp: A case study on url word breaking
Authors: Kuansan Wang, Christopher Thrasher, Bo-June Paul Hsu
Venue: Conference WWW 2011
Year: 2011

This paper uses the URL word breaking task as an example to elaborate what we identify as crucial in designing statistical natural language processing (NLP) algorithms for Web scale applications: (1) rudimentary multilingual capabilities to cope with the global nature of the Web, (2) multi-style modeling to handle diverse language styles seen in the Web contents, (3) fast adaptation to keep pace with the dynamic changes of the Web, (4) minimal heuristic assumptions for generalizability and robustness, and (5) possibilities of efficient implementations and minimal manual efforts for processing massive amount of data at a reasonable cost. We first show that the state-of-the-art word breaking techniques can be unified and generalized under the Bayesian minimum risk (BMR) framework that, using a Web scale N-gram, can meet the first three requirements. We discuss how the existing techniques can be viewed as introducing additional assumptions to the basic BMR framework, and describe a generic yet efficient implementation called word synchronous beam search. Testing the framework and its implementation on a series of large scale experiments reveals the following. First, the language style used to build the model plays a critical role in the word breaking task, and the most suitable for the URL word breaking task appears to be that of the document title where the best performance is obtained. Models created from other language styles, such as from document body, anchor text, and even queries, exhibit varying degrees of mismatch. Although all styles benefit from increasing modeling power which, in our experiments, corresponds to the use of a higher order N-gram, the gain is most recognizable for the title model. The heuristics proposed by the prior arts do contribute to the word breaking performance for mismatched or less powerful models, but are less effective and, in many cases, lead to poorer performance than the matched model with minimal assumptions. For the matched model based on document titles, an accuracy rate of 97.18% can already be achieved using simple trigram without any heuristics.
Neural Review Rating Prediction with User and Product Memory
Authors: Zhigang Yuan, Fangzhao Wu, Junxin Liu, Chuhan Wu, Yongfeng Huang, Xing Xie
Venue: Conference CIKM 2019
Year: 2019

Neural network methods have achieved great success in sentiment classification. Recent studies have found that incorporating user and product information can effectively improve the performance of review sentiment classification. However, most of these studies only concentrate on the influence of users and products, ignoring the inherent correlation between users or products. This information is important for users or products since they can obtain more information from similar users or products. In this paper, we propose a novel framework for review rating prediction with user and product memory. First, besides the original user or product representations, we construct inferred representations from representative users or products which are stored in memory slots. These memory units can be viewed as refined knowledge representations of users or products learned from the data. Then, we employ two hierarchical networks with user attention and product attention using both the original and inferred representations. Experiments on benchmark datasets show that our method can achieve state-of-the-art performance. Besides, our approach performs much more better in cold-start scenarios where the training data is scarce.
T-rex: A topic-aware relation extraction model
Authors: Woohwan Jung, Kyuseok Shim
Venue: Conference CIKM 2020
Year: 2020

Document-level relation extraction (RE) has recently received a lot of attention. However, existing models for document-level RE have similar structures to the models for sentence-level RE. Thus, they still do not consider some unique characteristics of the new problem setting. For example, in Wikipedia, there is a title for each page and it usually represents the topic entity that is mainly described on the page. In many cases, the topic entity is omitted in the text. Thus, existing RE models often fail to find the relations with the omitted topic entity. To tackle the problem, we propose a Topic-aware Relation EXtraction (T-REX) model. To extract the relations with the (possibly omitted) topic entity, the proposed model first encodes the topic entity by aggregating the information of all its mentions in the document. Then it finds the relations between the topic entity and each mention of other entities. Finally, the output layer combines the mention-wise results and outputs all relations expressed in the document. Our performance study with a large-scale dataset confirms the effectiveness of the T-REX model.
Generating clinical queries from patient narratives: a comparison between machines and humans
Authors: Bevan Koopman, Liam Cripwell, Guido Zuccon
Venue: Conference SIGIR 2017
Year: 2017

This paper investigates how automated query generation methods can be used to derive effective ad-hoc queries from verbose patient narratives. In a clinical setting, automatic query generation provides a means of retrieving information relevant to a clinician, based on a patient record, but without the need for the clinician to manually author a query. Given verbose patient narratives, we evaluated a number of query reduction methods, both generic and domain specific. Comparison was made against human generated queries, both in terms of retrieval effectiveness and characteristics of human queries. Query reduction was an effective means of generating ad-hoc queries from narratives. However, human generated queries were still significantly more effective than automatically generated queries. Further improvements were possible if parameters of the query reduction methods were set on a per-query basis and a means of predicting this was developed. Under ideal conditions, automated methods can exceed humans. Effective human queries were found to contain many novel keywords not found in the narrative. Automated reduction methods may be handicapped in that they only use terms from narrative. Future work, therefore, may be directed toward better understanding effective human queries and automated query rewriting methods that attempt to model the inference of novel terms by exploiting semantic inference processes.
Generalized zero-shot intent detection via commonsense knowledge
Authors: A. B. Siddique 0001, Fuad T. Jamour, Luxun Xu, Vagelis Hristidis
Venue: Conference SIGIR 2021
Year: 2021

Identifying user intents from natural language utterances is a crucial step in conversational systems that has been extensively studied as a supervised classification problem. However, in practice, new intents emerge after deploying an intent detection model. Thus, these models should seamlessly adapt and classify utterances with both seen and unseen intents -- unseen intents emerge after deployment and they do not have training data. The few existing models that target this setting rely heavily on the scarcely available training data and overfit to seen intents data, resulting in a bias to misclassify utterances with unseen intents into seen ones. We propose RIDE: an intent detection model that leverages commonsense knowledge in an unsupervised fashion to overcome the issue of training data scarcity. RIDE computes robust and generalizable relationship meta-features that capture deep semantic relationships between utterances and intent labels; these features are computed by considering how the concepts in an utterance are linked to those in an intent label via commonsense knowledge. Our extensive experimental analysis on three widely-used intent detection benchmarks shows that relationship meta-features significantly increase the accuracy of detecting both seen and unseen intents and that RIDE outperforms the state-of-the-art model for unseen intents.
Semi-supervised learning and optimization for hypergraph matching
Authors: Marius Leordeanu, Andrei Zanfir, Cristian Sminchisescu
Venue: Conference ICCV 2011
Year: 2011

Graph and hypergraph matching are important problems in computer vision. They are successfully used in many applications requiring 2D or 3D feature matching, such as 3D reconstruction and object recognition. While graph matching is limited to using pairwise relationships, hypergraph matching permits the use of relationships between sets of features of any order. Consequently, it carries the promise to make matching more robust to changes in scale, deformations and outliers. In this paper we make two contributions. First, we present a first semi-supervised algorithm for learning the parameters that control the hypergraph matching model and demonstrate experimentally that it significantly improves the performance of current state-of-the-art methods. Second, we propose a novel efficient hypergraph matching algorithm, which outperforms the state-of-the-art, and, when used in combination with other higher-order matching algorithms, it consistently improves their performance.
Clc-rs: A chinese legal case retrieval system with masked language ranking
Authors: Jieke Li, Min Yang, Chengming Li
Venue: Conference CIKM 2021
Year: 2021

With the ever-increasing size of legal cases in China, relevant legal case retrieval given a user query has attracted considerable attention. Conventional keyword-based retrieval systems look for matching cases that contain one or more words specified by the user. However, keyword search is sharply focused on finding the exact terms specified in the query, making the retrieval systems miss many relevant documents. In addition, it is difficult for new users to identify appropriate keywords for accurate legal case retrieval. In this paper, we develop a novel Chinese legal case retrieval system (called CLC-RS), which improves the quality of semantic search with natural language queries in the legal domain. CLC-RS performs legal case retrieval in a two-stage fashion. First, we employ a classic token-based ranking method to efficiently reduce the solution space, returning a subset of candidate legal cases. Then, we deploy a novel masked language ranking model to re-rank the candidate legal cases. The experimental results show that the proposed system is both efficient and effective, providing a practical information retrieval (IR) system for retrieving Chinese legal cases. The web site for the developed CLC-RS system is available at: https://www.delilegal.com/.
Syntactic features in question answering
Authors: Xiaoyan Li
Venue: Conference SIGIR 2003
Year: 2003

Syntactic information potentially plays a much more important role in question answering than it does in information retrieval. Although many people have used syntactic evidence in Question Answering, there haven't been many detailed experiments reported in the literature. The aim of the experiment described in this paper is to study the impact of a particular approach for using syntactic information on question answering effectiveness. Our results indicate that a combination of syntactic information with heuristics for ranking potential answers can perform better than the ranking heuristics on their own.
Why attentions may not be interpretable?
Authors: Bing Bai, Jian Liang, Guanhua Zhang, Hao Li, Kun Bai, Fei Wang
Venue: Conference KDD 2021
Year: 2021

Attention-based methods have played important roles in model interpretations, where the calculated attention weights are expected to highlight the critical parts of inputs (e.g., keywords in sentences). However, recent research found that attention-as-importance interpretations often do not work as we expected. For example, learned attention weights sometimes highlight less meaningful tokens like "[SEP]", ",", and ".", and are frequently uncorrelated with other feature importance indicators like gradient-based measures. A recent debate over whether attention is an explanation or not has drawn considerable interest. In this paper, we demonstrate that one root cause of this phenomenon is the combinatorial shortcuts, which means that, in addition to the highlighted parts, the attention weights themselves may carry extra information that could be utilized by downstream models after attention layers. As a result, the attention weights are no longer pure importance indicators. We theoretically analyze combinatorial shortcuts, design one intuitive experiment to show their existence, and propose two methods to mitigate this issue. We conduct empirical studies on attention-based interpretation models. The results show that the proposed methods can effectively improve the interpretability of attention mechanisms.
Unsupervised clinical language translation
Authors: Wei-Hung Weng, Yu-An Chung, Peter Szolovits
Venue: Conference KDD 2019
Year: 2019

As patients' access to their doctors' clinical notes becomes common, translating professional, clinical jargon to layperson-understandable language is essential to improve patient-clinician communication. Such translation yields better clinical outcomes by enhancing patients' understanding of their own health conditions, and thus improving patients' involvement in their own care. Existing research has used dictionary-based word replacement or definition insertion to approach the need. However, these methods are limited by expert curation, which is hard to scale and has trouble generalizing to unseen datasets that do not share an overlapping vocabulary. In contrast, we approach the clinical word and sentence translation problem in a completely unsupervised manner. We show that a framework using representation learning, bilingual dictionary induction and statistical machine translation yields the best precision at 10 of 0.827 on professional-to-consumer word translation, and mean opinion scores of 4.10 and 4.28 out of 5 for clinical correctness and layperson readability, respectively, on sentence translation. Our fully-unsupervised strategy overcomes the curation problem, and the clinically meaningful evaluation reduces biases from inappropriate evaluators, which are critical in clinical machine learning.
Decoding as dynamic programming for recurrent autoregressive models
Authors: Najam Zaidi, Trevor Cohn, Gholamreza Haffari
Venue: Conference ICLR 2020
Year: 2020

Decoding in autoregressive models (ARMs) consists of searching for a high scoring output sequence under the trained model. Standard decoding methods, based on unidirectional greedy algorithm or beam search, are suboptimal due to error propagation and myopic decisions which do not account for future steps in the generation process. In this paper we present a novel decoding approach based on the method of auxiliary coordinates (Carreira-Perpinan & Wang, 2014) to address the aforementioned shortcomings. Our method introduces discrete variables for output tokens, and auxiliary continuous variables representing the states of the underlying ARM. The auxiliary variables lead to a factor graph approximation of the ARM, whose maximum a posteriori (MAP) solution is found exactly using dynamic programming. The MAP solution is then used to recreate an improved factor graph approximation of the ARM via updated auxiliary variables. We then extend our approach to decode in an ensemble of ARMs, possibly with different generation orders, which is out of reach for the standard unidirectional decoding algorithms. Experiments on the text infilling task over SWAG and Daily Dialogue datasets show that our decoding method is superior to strong competing decoding methods.
The rise of guardians: Fact-checking URL recommendation to combat fake news
Authors: Nguyen Vo, Kyumin Lee
Venue: Conference SIGIR 2018
Year: 2018

A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11%~33%. Our source code and dataset are available at http://web.cs.wpi.edu/~kmlee/data/gau.html.
Adaptkt: A domain adaptable method for knowledge tracing
Authors: Song Cheng, Qi Liu, Enhong Chen, Kai Zhang, Zhenya Huang, Yu Yin, Xiaoqing Huang, Yu Su
Venue: Conference WSDM 2022
Year: 2022

Knowledge tracing is a crucial and fundamental task in online education systems, which can predict students' knowledge state for personalized learning. Unfortunately, existing methods are domain-specific, whereas there are many domains (e.g., subjects, schools) in the real education scene and some domains suffer from the problem of lacking sufficient data. Therefore, how to exploit the knowledge in other domains, to improve the model's performance for target domain remains pretty much open. We term this problem as Domain Adaptation for Knowledge Tracing (DAKT), which aims to transfer knowledge from the source domain to the target one for knowledge tracing. In this paper, we propose a novel adaptable method, namely Adaptable Knowledge Tracing (AdaptKT), which contains three phases to explore this problem. Specifically, phase I is instance selection. Given the question texts of two domains, we train an auto-encoder to select and embed similar instances from both domains. Phase II is distribution discrepancy minimizing. After obtaining the selected instances and their linguistic representations, we train a knowledge tracing model and adopt the Maximum Mean Discrepancy (MMD) to minimize the discrepancy between the distributions of the domain-specific knowledge states. Phase III is fine-tuning of the output layer. We replace the output layer of the model that trained in phase II by a new one to make the knowledge tracing model's output dimension matches the number of knowledge concepts in the target domain. The new output layer is trained while other parameters that before it are frozen. We conduct extensive experiments on two large-scale real-world datasets, where the experimental results clearly demonstrate the effectiveness of AdaptKT for solving DAKT problem. We will public the code on the Github after the acceptance of the paper.
Hate speech detection is not as easy as you may think: A closer look at model validation
Authors: Aymé Arango, Jorge Pérez, Barbara Poblete
Venue: Conference SIGIR 2019
Year: 2019

Hate speech is an important problem that is seriously affecting the dynamics and usefulness of online social communities. Large scale social platforms are currently investing important resources into automatically detecting and classifying hateful content, without much success. On the other hand, the results reported by state-of-the-art systems indicate that supervised approaches achieve almost perfect performance but only within specific datasets. In this work, we analyze this apparent contradiction between existing literature and actual applications. We study closely the experimental methodology used in prior work and their generalizability to other datasets. Our findings evidence methodological issues, as well as an important dataset bias. As a consequence, performance claims of the current state-of-the-art have become significantly overestimated. The problems that we have found are mostly related to data overfitting and sampling issues. We discuss the implications for current research and re-conduct experiments to give a more accurate picture of the current state-of-the art methods.
Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization
Authors: Ankan Saha, Vikas Sindhwani
Venue: Conference WSDM 2012
Year: 2012

As massive repositories of real-time human commentary, social media platforms have arguably evolved far beyond passive facilitation of online social interactions. Rapid analysis of information content in online social media streams (news articles, blogs,tweets etc.) is the need of the hour as it allows business and government bodies to understand public opinion about products and policies. In most of these settings, data points appear as a stream of high dimensional feature vectors. Guided by real-world industrial deployment scenarios, we revisit the problem of online learning of topics from streaming social media content. On one hand, the topics need to be dynamically adapted to the statistics of incoming datapoints, and on the other hand, early detection of rising new trends is important in many applications. We propose an online nonnegative matrix factorizations framework to capture the evolution and emergence of themes in unstructured text under a novel temporal regularization framework. We develop scalable optimization algorithms for our framework, propose a new set of evaluation metrics, and report promising empirical results on traditional TDT tasks as well as streaming Twitter data. Our system is able to rapidly capture emerging themes, track existing topics over time while maintaining temporal consistency and continuity in user views, and can be explicitly configured to bound the amount of information being presented to the user.
Fast and space-efficient entity linking for queries
Authors: Roi Blanco, Giuseppe Ottaviano, Edgar Meij
Venue: Conference WSDM 2015
Year: 2015

Entity linking deals with identifying entities from a knowledge base in a given piece of text and has become a fundamental building block for web search engines, enabling numerous downstream improvements from better document ranking to enhanced search results pages. A key problem in the context of web search queries is that this process needs to run under severe time constraints as it has to be performed before any actual retrieval takes place, typically within milliseconds.
Uncovering crowdsourced manipulation of online reviews
Authors: Amir Fayazi, Kyumin Lee, James Caverlee, Anna Cinzia Squicciarini
Venue: Conference SIGIR 2015
Year: 2015

Online reviews are a cornerstone of consumer decision making. However, their authenticity and quality has proven hard to control, especially as polluters target these reviews toward promoting products or in degrading competitors. In a troubling direction, the widespread growth of crowdsourcing platforms like Mechanical Turk has created a large-scale, potentially difficult-to-detect workforce of malicious review writers. Hence, this paper tackles the challenge of uncovering crowdsourced manipulation of online reviews through a three-part effort: (i) First, we propose a novel sampling method for identifying products that have been targeted for manipulation and a seed set of deceptive reviewers who have been enlisted through crowdsourcing platforms. (ii) Second, we augment this base set of deceptive reviewers through a reviewer-reviewer graph clustering approach based on a Markov Random Field where we define individual potentials (of single reviewers) and pair potentials (between two reviewers). (iii) Finally, we embed the results of this probabilistic model into a classification framework for detecting crowd-manipulated reviews. We find that the proposed approach achieves up to 0.96 AUC, outperforming both traditional detection methods and a SimRank-based alternative clustering approach.