OpenRaiser/Intern-Atlas huggingface dataset:
Distilling word embeddings: An encoding approach
Authors: Lili Mou, Ran Jia, Yan Xu, Ge Li, Lu Zhang, Zhi Jin
Venue: Conference CIKM 2016
Year: 2016

Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks. We propose an encoding approach to distill task-specific knowledge from a set of high-dimensional embeddings, which can reduce model complexity by a large margin as well as retain high accuracy, showing a good compromise between efficiency and performance. Experiments in two tasks reveal the phenomenon that distilling knowledge from cumbersome embeddings is better than directly training neural networks with small embeddings.
Transfer learning for sequence tagging with hierarchical recurrent networks
Authors: Zhilin Yang 0001, Ruslan Salakhutdinov, William W. Cohen
Venue: Conference ICLR 2017
Year: 2017

Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering. However, it is unclear if such systems can be used for tasks without large amounts of training data. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., POS tagging on Penn Treebank) is used to improve performance on a target task with fewer available annotations (e.g., POS tagging for microblogs). We examine the effects of transfer learning for deep hierarchical recurrent networks across domains, applications, and languages, and show that significant improvement can often be obtained. These improvements lead to improvements over the current state-of-the-art on several well-studied tasks.
You are where you tweet: a content-based approach to geo-locating Twitter users
Authors: Zhiyuan Cheng, James Caverlee, Kyumin Lee
Venue: Conference CIKM 2010
Year: 2010

We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues. By augmenting the massive human-powered sensing capabilities of Twitter and related microblogging services with content-derived location information, this framework can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on. Three of the key features of the proposed approach are: (i) its reliance purely on tweet content, meaning no need for user IP information, private login information, or external knowledge bases; (ii) a classification component for automatically identifying words in tweets with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. The system estimates k possible locations for each user in descending order of confidence. On average we find that the location estimates converge quickly (needing just 100s of tweets), placing 51% of Twitter users within 100 miles of their actual location.
A holistic lexicon-based approach to opinion mining
Authors: Xiaowen Ding, Bing Liu, Philip S. Yu
Venue: Conference WSDM 2008
Year: 2008

One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Most existing techniques utilize a list of opinion (bearing) words (also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc) states. These approaches, however, all have some major shortcomings. In this paper, we propose a holistic lexicon-based approach to solving the problem by exploiting external evidences and linguistic conventions of natural language expressions. This approach allows the system to handle opinion words that are context dependent, which cause major difficulties for existing algorithms. It also deals with many special words, phrases and language constructs which have impacts on opinions based on their linguistic patterns. It also has an effective function for aggregating multiple conflicting opinion words in a sentence. A system, called Opinion Observer, based on the proposed technique has been implemented. Experimental results using a benchmark product review data set and some additional reviews show that the proposed technique is highly effective. It outperforms existing methods significantly
Understanding and predicting importance in images
Authors: Alexander C. Berg, Tamara L. Berg, Hal Daumé III, Jesse Dodge, Amit Goyal 0001, Xufeng Han, Alyssa C. Mensch, Margaret Mitchell, Aneesh Sood, Karl Stratos, Kota Yamaguchi
Venue: Conference CVPR 2012
Year: 2012

What do people care about in an image? To drive computational visual recognition toward more human-centric outputs, we need a better understanding of how people perceive and judge the importance of content in images. In this paper, we explore how a number of factors relate to human perception of importance. Proposed factors fall into 3 broad types: 1) factors related to composition, e.g. size, location, 2) factors related to semantics, e.g. category of object or scene, and 3) contextual factors related to the likelihood of attribute-object, or object-scene pairs. We explore these factors using what people describe as a proxy for importance. Finally, we build models to predict what will be described about an image given either known image content, or image content estimated automatically by recognition systems.
Ex machina: personal attacks seen at scale
Authors: Ellery Wulczyn, Nithum Thain, Lucas Dixon
Venue: Conference WWW 2017
Year: 2017

The damage personal attacks cause to online discourse motivates many platforms to try to curb the phenomenon. However, understanding the prevalence and impact of personal attacks in online platforms at scale remains surprisingly difficult. The contribution of this paper is to develop and illustrate a method that combines crowdsourcing and machine learning to analyze personal attacks at scale. We show an evaluation method for a classifier in terms of the aggregated number of crowd-workers it can approximate. We apply our methodology to English Wikipedia, generating a corpus of over 100k high quality human-labeled comments and 63M machine-labeled ones from a classifier that is as good as the aggregate of 3 crowd-workers, as measured by the area under the ROC curve and Spearman correlation. Using this corpus of machine-labeled scores, our methodology allows us to explore some of the open questions about the nature of online personal attacks. This reveals that the majority of personal attacks on Wikipedia are not the result of a few malicious users, nor primarily the consequence of allowing anonymous contributions from unregistered users.
A combined pose, object, and feature model for action understanding
Authors: Benjamin Packer, Kate Saenko, Daphne Koller
Venue: Conference CVPR 2012
Year: 2012

Understanding natural human activity involves not only identifying the action being performed, but also locating the semantic elements of the scene and describing the person's interaction with them. We present a system that is able to recognize complex, fine-grained human actions involving the manipulation of objects in realistic action sequences. Our method takes advantage of recent advances in sensors and pose trackers in learning an action model that draws on successful discriminative techniques while explicitly modeling both pose trajectories and object manipulations. By combining these elements in a single model, we are able to simultaneously recognize actions and track the location and manipulation of objects. To showcase this ability, we introduce a novel Cooking Action Dataset that contains video, depth readings, and pose tracks from a Kinect sensor. We show that our model outperforms existing state of the art techniques on this dataset as well as the VISINT dataset with only video sequences.
Entity-centric topic-oriented opinion summarization in twitter
Authors: Xinfan Meng, Furu Wei, Xiaohua Liu, Ming Zhou, Sujian Li, Houfeng Wang
Venue: Conference KDD 2012
Year: 2012

Microblogging services, such as Twitter, have become popular channels for people to express their opinions towards a broad range of topics. Twitter generates a huge volume of instant messages (i.e. tweets) carrying users' sentiments and attitudes every minute, which both necessitates automatic opinion summarization and poses great challenges to the summarization system. In this paper, we study the problem of opinion summarization for entities, such as celebrities and brands, in Twitter. We propose an entity-centric topic-based opinion summarization framework, which aims to produce opinion summaries in accordance with topics and remarkably emphasizing the insight behind the opinions. To this end, we first mine topics from #hashtags, the human-annotated semantic tags in tweets. We integrate the #hashtags as weakly supervised information into topic modeling algorithms to obtain better interpretation and representation for calculating the similarity among them, and adopt Affinity Propagation algorithm to group #hashtags into coherent topics. Subsequently, we use templates generalized from paraphrasing to identify tweets with deep insights, which reveal reasons, express demands or reflect viewpoints. Afterwards, we develop a target (i.e. entity) dependent sentiment classification approach to identifying the opinion towards a given target (i.e. entity) of tweets. Finally, the opinion summary is generated through integrating information from dimensions of topic, opinion and insight, as well as other factors (e.g. topic relevancy, redundancy and language styles) in an unified optimization framework. We conduct extensive experiments on a real-life data set to evaluate the performance of individual opinion summarization modules as well as the quality of the produced summary. The promising experiment results show the effectiveness of the proposed framework and algorithms.
Exploring the space of topic coherence measures
Authors: Michael Röder, Andreas Both, Alexander Hinneburg
Venue: Conference WSDM 2015
Year: 2015

Quantifying the coherence of a set of statements is a long standing problem with many potential applications that has attracted researchers from different sciences. The special case of measuring coherence of topics has been recently studied to remedy the problem that topic models give no guaranty on the interpretablity of their output. Several benchmark datasets were produced that record human judgements of the interpretability of topics. We are the first to propose a framework that allows to construct existing word based coherence measures as well as new ones by combining elementary components. We conduct a systematic search of the space of coherence measures using all publicly available topic relevance data for the evaluation. Our results show that new combinations of components outperform existing measures with respect to correlation to human ratings. nFinally, we outline how our results can be transferred to further applications in the context of text mining, information retrieval and the world wide web.
Hiexpan: Task-guided taxonomy construction by hierarchical tree expansion
Authors: Jiaming Shen, Zeqiu Wu, Dongming Lei, Chao Zhang, Xiang Ren, Michelle T. Vanni, Brian M. Sadler, Jiawei Han
Venue: Conference KDD 2018
Year: 2018

Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most existing automatic taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the is-a relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus, and allow users to input a seed taxonomy, serving as the task guidance. We propose an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy. Specifically, HiExpan views all children under each taxonomy node forming a coherent set and builds the taxonomy by recursively expanding all these sets. Furthermore, HiExpan incorporates a weakly-supervised relation extraction module to extract the initial children of a newly-expanded node and adjusts the taxonomy tree by optimizing its global structure. Our experiments on three real datasets from different domains demonstrate the effectiveness of HiExpan for building task-guided taxonomies.
Multi-task pharmacovigilance mining from social media posts
Authors: Shaika Chowdhury, Chenwei Zhang, Philip S. Yu
Venue: Conference WWW 2018
Year: 2018

Social media has grown to be a crucial information source for pharmacovigilance studies where an increasing number of people post adverse reactions to medical drugs that are previously unreported. Aiming to effectively monitor various aspects of Adverse Drug Reactions (ADRs) from diversely expressed social medical posts, we propose a multi-task neural network framework that learns several tasks associated with ADR monitoring with different levels of supervisions collectively. Besides being able to correctly classify ADR posts and accurately extract ADR mentions from online posts, the proposed framework is also able to further understand reasons for which the drug is being taken, known as »indications», from the given social media post. A coverage-based attention mechanism is adopted in our framework to help the model properly identify »phrasal» ADRs and Indications that are attentive to multiple words in a post. Our framework is applicable in situations where limited parallel data for different pharmacovigilance tasks are available. We evaluate the proposed framework on real-world Twitter datasets, where the proposed model outperforms the state-of-the-art alternatives of each individual task consistently.
Identifying Users behind Shared Accounts in Online Streaming Services
Authors: Jyun-Yu Jiang, Cheng-Te Li, Yian Chen, Wei Wang
Venue: Conference SIGIR 2018
Year: 2018

Online streaming services are prevalent. Major service providers, such as Netflix (for movies) and Spotify (for music), usually have a large customer base. More often than not, users may share an account. This has attracted increasing attention recently, as account sharing not only compromises the service provider's financial interests but also impairs the performance of recommendation systems and consequently the quality of service provided to the users. To address this issue, this paper focuses on the problem of user identification in shared accounts. Our goal is three-fold: (1) Given an account, along with its historical session logs, we identify a set of users who share such account; (2) Given a new session issued by an account, we find the corresponding user among the identified users of such account; (3) We aim to boost the performance of item recommendation by user identification. While the mapping between users and accounts is unknown, we propose an unsupervised learning-based framework, Session-based Heterogeneous graph Embedding for User Identification (SHE-UI), to differentiate and model the preferences of users in an account, and to group sessions by these users. In SHE-UI, a heterogeneous graph is constructed to represent items such as songs and their available metadata such as artists, genres, and albums. An item-based session embedding technique is proposed using a normalized random walk in the heterogeneous graph. Our experiments conducted on two large-scale music streaming datasets, Last.fm and KKBOX, show that SHE-UI not only accurately identifies users, but also significantly improves the performance of item recommendation over the state-of-the-art methods.
Leveraging the implicit structure within social media for emergent rumor detection
Authors: Justin Sampson, Fred Morstatter, Liang Wu, Huan Liu
Venue: Conference CIKM 2016
Year: 2016

The automatic and early detection of rumors is of paramount importance as the spread of information with questionable veracity can have devastating consequences. This became starkly apparent when, in early 2013, a compromised Associated Press account issued a tweet claiming that there had been an explosion at the White House. This tweet resulted in a significant drop for the Dow Jones Industrial Average. Most existing work in rumor detection leverages conversation statistics and propagation patterns, however, such patterns tend to emerge slowly requiring a conversation to have a significant number of interactions in order to become eligible for classification. In this work, we propose a method for classifying conversations within their formative stages as well as improving accuracy within mature conversations through the discovery of implicit linkages between conversation fragments. In our experiments, we show that current state-of-the-art rumor classification methods can leverage implicit links to significantly improve the ability to properly classify emergent conversations when very little conversation data is available. Adopting this technique allows rumor detection methods to continue to provide a high degree of classification accuracy on emergent conversations with as few as a single tweet. This improvement virtually eliminates the delay of conversation growth inherent in current rumor classification methods while significantly increasing the number of conversations considered viable for classification.
Threading machine generated email
Authors: Nir Ailon, Zohar Shay Karnin, Edo Liberty, Yoelle Maarek
Venue: Conference WSDM 2013
Year: 2013

Viewing email messages as parts of a sequence or a thread is a convenient way to quickly understand their context. Current threading techniques rely on purely syntactic methods, matching sender information, subject line, and reply/forward prefixes. As such, they are mostly limited to personal conversations. In contrast, machine-generated email, which amount, as per our experiments, to more than 60% of the overall email traffic, requires a different kind of threading that should reflect how a sequence of emails is caused by a few related user actions. For example, purchasing goods from an online store will result in a receipt or a confirmation message, which may be followed, possibly after a few days, by a shipment notification message from an express shipping service. In today's mail systems, they will not be a part of the same thread, while we believe they should. In this paper, we focus on this type of threading that we coin "causal threading". We demonstrate that, by analyzing recurring patterns over hundreds of millions of mail users, we can infer a causality relation between these two individual messages. In addition, by observing multiple causal relations over common messages, we can generate "causal threads" over a sequence of messages. The four key stages of our approach consist of: (1) identifying messages that are instances of the same email type or "template" (generated by the same machine process on the sender side) (2) building a causal graph, in which nodes correspond to email templates and edges indicate potential causal relations (3) learning a causal relation prediction function, and (4) automatically "threading" the incoming email stream. We present detailed experimental results obtained by analyzing the inboxes of 12.5 million Yahoo! Mail users, who voluntarily opted-in for such research. Supervised editorial judgments show that we can identify more than 70% (recall rate) of all "causal threads" at a precision level of 90%. In addition, for a search scenario we show that we achieve a precision close to 80% at 90% recall. We believe that supporting causal threads in email clients opens new grounds for improving both email search and browsing experiences.
More than threads: Identifying related email messages
Authors: Noa Avigdor-Elgrabli, Roei Gelbhart, Irena Grabovitch-Zuyev, Ariel Raviv
Venue: Conference CIKM 2018
Year: 2018

In the typical state of an ever growing mailbox, it becomes essential to assist the user to better organize and quickly look up the content of his electronic life. Our work addresses this challenge, by identifying related messages within a user's mailbox. We study the notion of semantic relatedness between email messages and aim to offer the user with a wider context of the message he selects or reads. The context is represented by a small set of messages that are semantically related to the given message. We conduct experiments on a large-scale mail dataset obtained from a major Web mail service and demonstrate the effectiveness of our model in this task.
Automated Extractions for Machine Generated Mail
Authors: Dotan Di Castro, Iftah Gamzu, Irena Grabovitch-Zuyev, Liane Lewin-Eytan, Abhinav Pundir, Nil Ratan Sahoo, Michael Viderman
Venue: Conference WWW 2018
Year: 2018

Mail extraction is a critical task whose objective is to extract valuable data from the content of mail messages. This task is key for many types of applications including re-targeting, mail search, and mail summarization, which utilize the important personal data pieces in mail messages to achieve their objectives. We focus on machine generated traffic, which comprises most of the Web mail traffic today, and use its structured and large-scale repetitive nature to devise a fully automated extraction method. Our solution builds on an advanced structural clustering technique previously presented by some of the authors of this work. The heart of our solution is an offline process that leverages the structural mail-specific characteristics of the clustering, and automatically creates extraction rules that are later applied online for each new arriving message. We provide of a full description of our process, which has been productized in Yahoo mail backend. We complete our work with large-scale experiments carried over real Yahoo mail traffic, and evaluate the performance of our automatic extraction method.