OpenRaiser/Intern-Atlas huggingface dataset:
Query based event extraction along a timeline
Authors: Hai Leong Chieu, Yoong Keok Lee
Venue: Conference SIGIR 2004
Year: 2004

In this paper, we present a framework and a system that extracts events relevant to a query from a collection C of documents, and places such events along a timeline. Each event is represented by a sentence extracted from C, based on the assumption that "important" events are widely cited in many documents for a period of time within which these events are of interest. In our experiments, we used queries that are event types ("earthquake") and person names (e.g. "George Bush"). Evaluation was performed using G8 leader names as queries: comparison made by human evaluators between manually and system generated timelines showed that although manually generated timelines are on average more preferable, system generated timelines are sometimes judged to be better than manually constructed ones.
Automatic generation of overview timelines
Authors: Russell C. Swan, James Allan
Venue: Conference SIGIR 2000
Year: 2000

We present a statistical model of feature occurrence over time, and develop tests based on classical hypothesis testing for significance of term appearance on a given date. Using additional classical hypothesis testing we are able to combine these terms to generate “topics” as defined by the Topic Detection and Tracking study. The groupings of terms obtained can be used to automatically generate an interactive timeline displaying the major events and topics covered by the corpus. To test the validity of our technique we extracted a large number of these topics from a test corpus and had human evaluators judge how well the selected features captured the gist of the topics, and how they overlapped with a set of known topics from the corpus. The resulting topics were highly rated by evaluators who compared them to known topics.
Evolutionary timeline summarization: A balanced optimization framework via iterative substitution
Authors: Rui Yan, Xiaojun Wan, Jahna Otterbacher, Liang Kong, Xiaoming Li, Yan Zhang
Venue: Conference SIGIR 2011
Year: 2011

Classic news summarization plays an important role with the exponential document growth on the Web. Many approaches are proposed to generate summaries but seldom simultaneously consider evolutionary characteristics of news plus to traditional summary elements. Therefore, we present a novel framework for the web mining problem named Evolutionary Timeline Summarization (ETS). Given the massive collection of time-stamped web documents related to a general news query, ETS aims to return the evolution trajectory along the timeline, consisting of individual but correlated summaries of each date, emphasizing relevance, coverage, coherence and cross-date diversity. ETS greatly facilitates fast news browsing and knowledge comprehension and hence is a necessity. We formally formulate the task as an optimization problem via iterative substitution from a set of sentences to a subset of sentences that satisfies the above requirements, balancing coherence/diversity measurement and local/global summary quality. The optimized substitution is iteratively conducted by incorporating several constraints until convergence. We develop experimental systems to evaluate on 6 instinctively different datasets which amount to 10251 documents. Performance comparisons between different system-generated timelines and manually created ones by human editors demonstrate the effectiveness of our proposed framework in terms of ROUGE metrics.
Identifying useful subgoals in reinforcement learning by local graph partitioning
Authors: Özgür Simsek, Alicia P. Wolfe, Andrew G. Barto
Venue: Conference ICML 2005
Year: 2005

We present a new subgoal-based method for automatically creating useful skills in reinforcement learning. Our method identifies subgoals by partitioning local state transition graphs---those that are constructed using only the most recent experiences of the agent. The local scope of our subgoal discovery method allows it to successfully identify the type of subgoals we seek---states that lie between two densely-connected regions of the state space while producing an algorithm with low computational cost.
Acquisition of instance attributes via labeled and related instances
Authors: Enrique Alfonseca, Marius Pasca, Enrique Robledo-Arnuncio
Venue: Conference SIGIR 2010
Year: 2010

This paper presents a method for increasing the quality of automatically extracted instance attributes by exploiting weakly-supervised and unsupervised instance relatedness data. This data consists of (a) class labels for instances and (b) distributional similarity scores. The method organizes the text-derived data into a graph, and automatically propagates attributes among related instances, through random walks over the graph. Experiments on various graph topologies illustrate the advantage of the method over both the original attribute lists and a per-class attribute extractor, both in terms of the number of attributes extracted per instance and the accuracy of the top-ranked attributes.
Clickthrough-based latent semantic models for web search
Authors: Jianfeng Gao, Kristina Toutanova, Wen-tau Yih
Venue: Conference SIGIR 2011
Year: 2011

This paper presents two new document ranking models for Web search based upon the methods of semantic representation and the statistical translation-based approach to information retrieval (IR). Assuming that a query is parallel to the titles of the documents clicked on for that query, large amounts of query-title pairs are constructed from clickthrough data; two latent semantic models are learned from this data. One is a bilingual topic model within the language modeling framework. It ranks documents for a query by the likelihood of the query being a semantics-based translation of the documents. The semantic representation is language independent and learned from query-title pairs, with the assumption that a query and its paired titles share the same distribution over semantic topics. The other is a discriminative projection model within the vector space modeling framework. Unlike Latent Semantic Analysis and its variants, the projection matrix in our model, which is used to map from term vectors into sematic space, is learned discriminatively such that the distance between a query and its paired title, both represented as vectors in the projected semantic space, is smaller than that between the query and the titles of other documents which have no clicks for that query. These models are evaluated on the Web search task using a real world data set. Results show that they significantly outperform their corresponding baseline models, which are state-of-the-art.
Determining the user intent of web search engine queries
Authors: Bernard J. Jansen, Danielle L. Booth, Amanda Spink
Venue: Conference WWW 2007
Year: 2007

Determining the user intent of Web searches is a difficult problem due to the sparse data available concerning the searcher. In this paper, we examine a method to determine the user intent underlying Web search engine queries. We qualitatively analyze samples of queries from seven transaction logs from three different Web search engines containing more than five million queries. From this analysis, we identified characteristics of user queries based on three broad classifications of user intent. The classifications of informational, navigational, and transactional represent the type of content destination the searcher desired as expressed by their query. We implemented our classification algorithm and automatically classified a separate Web search engine transaction log of over a million queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the classification to the results from our algorithm. This comparison showed that our automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is generally vague or multi-faceted, pointing to the need to for probabilistic classification. We illustrate how knowledge of searcher intent might be used to enhance future Web search engines.
Inferring query intent from reformulations and clicks
Authors: Filip Radlinski, Martin Szummer, Nick Craswell
Venue: Conference WWW 2010
Year: 2010

Many researchers have noted that web search queries are often ambiguous or unclear. We present an approach for identifying the popular meanings of queries using web search logs and user click behavior. We show our approach to produce more complete and user-centric intents than expert judges by evaluating on TREC queries. This approach was also used by the TREC 2009 Web Track judges to obtain more representative topic descriptions from real queries.
Acquiring ontological knowledge from query logs
Authors: Satoshi Sekine, Hisami Suzuki
Venue: Conference WWW 2007
Year: 2007

We present a method for acquiring ontological knowledge using search query logs. We first use query logs to identify important contexts associated with terms belonging to a semantic category; we then use these contexts to harvest new words belonging to this category. Our evaluation on selected categories indicates that the method works very well to help harvesting terms, achieving 85% to 95% accuracy in categorizing newly acquired terms.
Building taxonomy of web search intents for name entity queries
Authors: Xiaoxin Yin, Sarthak Shah
Venue: Conference WWW 2010
Year: 2010

A significant portion of web search queries are name entity queries. The major search engines have been exploring various ways to provide better user experiences for name entity queries, such as showing "search tasks" (Bing search) and showing direct answers (Yahoo!, Kosmix). In order to provide the search tasks or direct answers that can satisfy most popular user intents, we need to capture these intents, together with relationships between them.
Condorcet fusion for improved retrieval
Authors: Mark H. Montague, Javed A. Aslam
Venue: Conference CIKM 2002
Year: 2002

We present a new algorithm for improving retrieval results by combining document ranking functions: Condorcet-fuse. Beginning with one of the two major classes of voting procedures from Social Choice Theory, the Condorcet procedure, we apply a graph-theoretic analysis that yields a sorting-based algorithm that is elegant, efficient, and effective. The algorithm performs very well on TREC data, often outperforming existing metasearch algorithms whether or not relevance scores and training data is available. Condorcet-fuse significantly outperforms Borda-fuse, the analogous representative from the other major class of voting algorithms.
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Authors: Jiang Bian, Yandong Liu, Ding Zhou, Eugene Agichtein, Hongyuan Zha
Venue: Conference WWW 2009
Year: 2009

Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely- increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data – which
Summarization evaluation using relative utility
Authors: Dragomir R. Radev, Daniel Tam
Venue: Conference CIKM 2003
Year: 2003

We present a series of experiments to demonstrate the validity of Relative Utility (RU) as a measure for evaluating extractive summarizers. RU is applicable in both single-document and multi-document summarization, is extendable to arbitrary compression rates with no extra annotation effort, and takes into account both random system performance and interjudge agreement. Our results using the JHU summary corpus indicate that RU is a reasonable and often superior alternative to several common evaluation metrics.
Experiments on pattern-based relation learning
Authors: Willy Yap, Timothy Baldwin
Venue: Conference CIKM 2009
Year: 2009

Relation extraction is the task of extracting semantic relations - such as synonymy or hypernymy - between word pairs from corpus data. Past work in relation extraction has concentrated on manually creating templates to use in directly extracting word pairs for a given semantic relation from corpus text. Recently, there has been a move towards using machine learning to automatically learn these patterns. We build on this research by running experiments investigating the impact of corpus type, corpus size and different parameter settings on learning a range of lexical relations.
Unsupervised discovery of opposing opinion networks from forum discussions
Authors: Yue Lu, Hongning Wang, ChengXiang Zhai, Dan Roth
Venue: Conference CIKM 2012
Year: 2012

With more and more people freely express opinions as well as actively interact with each other in discussion threads, online forums are becoming a gold mine with rich information about people's opinions and social behaviors. In this paper, we study an interesting new problem of automatically discovering opposing opinion networks of users from forum discussions, which are subset of users who are strongly against each other on some topic. Toward this goal, we propose to use signals from both textual content (e.g., who says what) and social interactions (e.g., who talks to whom) which are both abundant in online forums. We also design an optimization formulation to combine all the signals in an unsupervised way. We created a data set by manually annotating forum data on five controversial topics and our experimental results show that the proposed optimization method outperforms several baselines and existing approaches, demonstrating the power of combining both text analysis and social network analysis in analyzing and generating the opposing opinion networks.
Towards a universal wordnet by learning from combined evidence
Authors: Gerard de Melo, Gerhard Weikum
Venue: Conference CIKM 2009
Year: 2009

Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification.
Mining slang and urban opinion words and phrases from cqa services: an optimization approach
Authors: Hadi Amiri, Tat-Seng Chua
Venue: Conference WSDM 2012
Year: 2012

Current opinion lexicons contain most of the common opinion words, but they miss slang and so-called urban opinion words and phrases (e.g. delish, cozy, yummy, nerdy, and yuck). These subjectivity clues are frequently used in community questions and are useful for opinion question analysis. This paper introduces a principled approach to constructing an opinion lexicon for community-based question answering (cQA) services. We formulate the opinion lexicon induction as a semi-supervised learning task in the graph context. Our method makes use of existing opinion words to extract new opinion entities (slang and urban words/phrases) from community questions. It then models the opinion entities in a graph context to learn the polarity of the new opinion entities based on the graph connectivity information. In contrast to previous approaches, our method not only learns such polarities from the labeled data but also from the unlabeled data and is more feasible in the web context where the dictionary-based relations (such as synonym, antonym, or hyponym) between most words are not available for constructing a high quality graph. The experiments show that our approach is effective both in terms of the quality of the discovered new opinion entities as well as its ability in inferring their polarity. Furthermore, since the value of opinion lexicons lies in their usefulness in applications, we show the utility of the constructed lexicon in the sentiment classification task.
Joint bilingual name tagging for parallel corpora
Authors: Qi Li, Haibo Li, Heng Ji, Wen Wang, Jing Zheng, Fei Huang
Venue: Conference CIKM 2012
Year: 2012

Traditional isolated monolingual name taggers tend to yield inconsistent results across two languages. In this paper, we propose two novel approaches to jointly and consistently extract names from parallel corpora. The first approach uses standard linear-chain Conditional Random Fields (CRFs) as the learning framework, incorporating cross-lingual features propagated between two languages. The second approach is based on a joint CRFs model to jointly decode sentence pairs, incorporating bilingual factors based on word alignment. Experiments on Chinese-English parallel corpora demonstrated that the proposed methods significantly outperformed monolingual name taggers, were robust to automatic alignment noise and achieved state-of-the-art performance. With only 20%of the training data, our proposed methods can already achieve better performance compared to the baseline learned from the whole training set.1
Smoothing techniques for adaptive online language models: topic tracking in tweet streams
Authors: Jimmy Lin, Rion Snow, William Morgan
Venue: Conference KDD 2011
Year: 2011

We are interested in the problem of tracking broad topics such as "baseball" and "fashion" in continuous streams of short texts, exemplified by tweets from the microblogging service Twitter. The task is conceived as a language modeling problem where per-topic models are trained using hashtags in the tweet stream, which serve as proxies for topic labels. Simple perplexity-based classifiers are then applied to filter the tweet stream for topics of interest. Within this framework, we evaluate, both intrinsically and extrinsically, smoothing techniques for integrating "foreground" models (to capture recency) and "background" models (to combat sparsity), as well as different techniques for retaining history. Experiments show that unigram language models smoothed using a normalized extension of stupid backoff and a simple queue for history retention performs well on the task.
Earthquake shakes twitter users: real-time event detection by social sensors
Authors: Takeshi Sakaki, Makoto Okazaki, Yutaka Matsuo
Venue: Conference WWW 2010
Year: 2010

Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.
Generating comparative summaries of contradictory opinions in text
Authors: Hyun Duk Kim, ChengXiang Zhai
Venue: Conference CIKM 2009
Year: 2009

This paper presents a study of a novel summarization problem called contrastive opinion summarization (COS). Given two sets of positively and negatively opinionated sentences which are often the output of an existing opinion summarizer, COS aims to extract comparable sentences from each set of opinions and generate a comparative summary containing a set of contrastive sentence pairs. We formally formulate the problem as an optimization problem and propose two general methods for generating a comparative summary using the framework, both of which rely on measuring the content similarity and contrastive similarity of two sentences. We study several strategies to compute these two similarities. We also create a test data set for evaluating such a novel summarization problem. Experiment results on this test set show that the proposed methods are effective for generating comparative summaries of contradictory opinions.
Bursty and hierarchical structure in streams
Authors: Jon M. Kleinberg
Venue: Conference KDD 2002
Year: 2002

A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the following intuitive premise --- that the appearance of a topic in a document stream is signaled by a "burst of activity," with certain features rising sharply in frequency as the topic emerges.The goal of the present work is to develop a formal approach for modeling such "bursts," in such a way that they can be robustly and efficiently identified, and can provide an organizational framework for analyzing the underlying content. The approach is based on modeling the stream using an infinite-state automaton, in which bursts appear naturally as state transitions; in some ways, it can be viewed as drawing an analogy with models from queueing theory for bursty network traffic. The resulting algorithms are highly efficient, and yield a nested representation of the set of bursts that imposes a hierarchical structure on the overall stream. Experiments with e-mail and research paper archives suggest that the resulting structures have a natural meaning in terms of the content that gave rise to them.