Li Zheng, Xin Zhang, Shuyi He, Fei Li, Chong Teng, Jiang-Ming Yang, Donghong Ji, Zhuang Li. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Travel route planning is an important step for a tourist to prepare his/her trip. As a common scenario, a tourist usually asks the following questions when he/she is planning his/her trip in an unfamiliar place: 1) Are there any travel route suggestions for a one-day or three-day trip in Beijing? 2) What is the most popular travel path within the Forbidden City? To facilitate a tourist's trip planning, in this paper, we target at solving the problem of automatic travel route planning. We propose to leverage existing travel clues recovered from 20 million geo-tagged photos collected from www.panoramio.com to suggest customized travel route plans according to users' preferences. As the footprints of tourists at memorable destinations, the geo-tagged photos could be naturally used to discover the travel paths within a destination (attractions/landmarks) and travel routes between destinations. Based on the information discovered from geo-tagged photos, we can provide a customized trip plan for a tourist, i.e., the popular destinations to visit, the visiting order of destinations, the time arrangement in each destination, and the typical travel path within each destination. Users are also enabled to specify personal preference such as visiting location, visiting time/season, travel duration, and destination style in an interactive manner to guide the system. Owning to 20 million geo-tagged photos and 200,000 travelogues, an online system has been developed to help users plan travel routes for over 30,000 attractions/landmarks in more than 100 countries and territories. Experimental results show the intelligence and effectiveness of the proposed framework.

With the prosperity of tourism and Web 2.0 technologies, more and more people have willingness to share their travel experiences on the Web (e.g., weblogs, forums, or Web 2.0 communities). These so-called travelogues contain rich information, particularly including location-representative knowledge such as attractions (e.g., Golden Gate Bridge), styles (e.g., beach, history), and activities (e.g., diving, surfing). The location-representative information in travelogues can greatly facilitate other tourists' trip planning, if it can be correctly extracted and summarized. However, since most travelogues are unstructured and contain much noise, it is difficult for common users to utilize such knowledge effectively. In this paper, to mine location-representative knowledge from a large collection of travelogues, we propose a probabilistic topic model, named as Location-Topic model. This model has the advantages of (1) differentiability between two kinds of topics, i.e., local topics which characterize locations and global topics which represent other common themes shared by various locations, and (2) representation of locations in the local topic space to encode both location-representative knowledge and similarities between locations. Some novel applications are developed based on the proposed model, including (1) destination recommendation for on flexible queries, (2) characteristic summarization for a given destination with representative tags and snippets, and (3) identification of informative parts of a travelogue and enriching such highlights with related images. Based on a large collection of travelogues, the proposed framework is evaluated using both objective and subjective evaluation methods and shows promising results.

Duplicate URLs have brought serious troubles to the whole pipeline of a search engine, from crawling, indexing, to result serving. URL normalization is to transform duplicate URLs to a canonical form using a set of rewrite rules. Nowadays URL normalization has attracted significant attention as it is lightweight and can be flexibly integrated into both the online (e.g. crawling) and the offline (e.g. index compression) parts of a search engine. To deal with a large scale of websites, automatic approaches are highly desired to learn rewrite rules for various kinds of duplicate URLs. In this paper, we rethink the problem of URL normalization from a global perspective and propose a pattern tree-based approach, which is remarkably different from existing approaches. Most current approaches learn rewrite rules by iteratively inducing local duplicate pairs to more general forms, and inevitably suffer from noisy training data and are practically inefficient. Given a training set of URLs partitioned into duplicate clusters for a targeted website, we develop a simple yet efficient algorithm to automatically construct a URL pattern tree. With the pattern tree, the statistical information from all the training samples is leveraged to make the learning process more robust and reliable. The learning process is also accelerated as rules are directly summarized based on pattern tree nodes. In addition, from an engineering perspective, the pattern tree helps select deployable rules by removing conflicts and redundancies. An evaluation on more than 70 million duplicate URLs from 200 websites showed that the proposed approach achieves very promising performance, in terms of both de-duping effectiveness and computational efficiency.

We study in this paper the problem of incremental crawling of web forums, which is a very fundamental yet challenging step in many web applications. Traditional approaches mainly focus on scheduling the revisiting strategy of each individual page. However, simply assigning different weights for different individual pages is usually inefficient in crawling forum sites because of the different characteristics between forum sites and general websites. Instead of treating each individual page independently, we propose a list-wise strategy by taking into account the site-level knowledge. Such site-level knowledge is mined through reconstructing the linking structure, called sitemap, for a given forum site. With the sitemap, posts from the same thread but distributed on various pages can be concatenated according to their timestamps. After that, for each thread, we employ a regression model to predict the time when the next post arrives. Based on this model, we develop an efficient crawler which is 260% faster than some state-of-the-art methods in terms of fetching new generated content; and meanwhile our crawler also ensure a high coverage ratio. Experimental results show promising performance of Coverage, Bandwidth utilization, and Timeliness of our crawler on 18 various forums.

Automatically generating location overviews in the form of both visual and textual descriptions is highly desired for online services such as travel planning, to provide attractive and comprehensive outlines of travel destinations. Actually, user-generated content (e.g., travelogues) on the Web provides abundant information to various aspects (e.g., landmarks, styles, activities) of most locations in the world. To leverage the experience shared by Web users, in this paper we propose a location overview generation approach, which first mines location-representative tags from travelogues and then uses such tags to retrieve web images. The learnt tags and retrieved images are finally presented via a novel user interface which provides an informative overview for a given location. Experimental results based on 23,756 travelogues and evaluation over 20 locations show promising results on both travelogue mining and location overview generation.

In this paper, we propose a system called TravelScope that helps users experience virtual tours by presenting information mined from user-generated travelogues and photos. The system can (1) recommend popular places for a given region; (2) characterize comprehensive aspects (e.g., landmarks, styles, activities) for a location; and (3) show representative images for a landmark. A novel user interface is designed to provide a better user experience, by organizing both the textual and visual information generated by dedicated travelers in an attractive way.

The huge amount of knowledge in web communities has motivated the research interests in threaded discussions. The dynamic nature of threaded discussions poses lots of challenging problems for computer scientists. Although techniques such as semantic models and structural models have been shown to be useful in a number of areas, they are inefficient in understanding threaded discussions due to three reasons: (I) as most of users read existing messages before posting, posts in a discussion thread are temporally dependent on the previous ones; It causes the semantics and structure to be coupled with each other in threaded discussions; (II) in online discussion threads, there are a lot of junk posts which are useless and may disturb content analysis; and (III) it is very hard to judge the quality of a post. In this paper, we propose a sparse coding-based model named SMSS to Simultaneously Model Semantics and Structure of threaded discussions. The model projects each post into a topic space, and approximates each post by a linear combination of previous posts in the same discussion thread. Meanwhile, the model also imposes two sparse constraints to force a sparse post reconstruction in the topic space and a sparse post approximation from previous posts. The sparse properties effectively take into account the characteristics of threaded discussions. Towards the above three problems, we demonstrate the competency of our model in three applications: reconstructing reply structure of threaded discussions, identifying junk posts, and finding experts in a given board/sub-board in web communities. Experimental results show encouraging performance of the proposed SMSS model in all these applications.

The abundant knowledge in web communities has motivated the research interests in discussion threads. The dynamic nature of discussion threads poses interesting and challenging problems for computer scientists. Although techniques such as semantic models or structural models have been shown to be useful in a number of areas, they are inefficient in understanding discussion threads due to the temporal dependence among posts in a discussion thread. Such dependence causes that semantics and structure coupled with each other in discussion threads. In this paper, we propose a sparse coding-based model named SMSS to Simultaneously Model Semantic and Structure of discussion threads.

Web forums have become an important data resource for many web applications, but extracting structured data from unstructured web forum pages is still a challenging task due to both complex page layout designs and unrestricted user created posts. In this paper, we study the problem of structured data extraction from various web forum sites. Our target is to find a solution as general as possible to extract structured data, such as post title, post author, post time, and post content from any forum site. In contrast to most existing information extraction methods, which only leverage the knowledge inside an individual page, we incorporate both page-level and site-level knowledge and employ Markov logic networks (MLNs) to effectively integrate all useful evidence by learning their importance automatically. Site-level knowledge includes (1) the linkages among different object pages, such as list pages and post pages, and (2) the interrelationships of pages belonging to the same object. The experimental results on 20 forums show a very encouraging information extraction performance, and demonstrate the ability of the proposed approach on various forums. We also show that the performance is limited if only page-level knowledge is used, while when incorporating the site-level knowledge both precision and recall can be significantly improved.

In this paper, we proposed a unified strategy to combine query log and search results for query suggestion. In this way, we leverage both the users' search intentions for popular queries and the power of search engines for unpopular queries. The suggested queries are also ranked according to their relevance and qualities; and each suggestion is described with a rich snippet including a photo and related description.

In this paper, we study the problem of Web forum crawling. Web forum has now become an important data source of many Web applications; while forum crawling is still a challenging task due to complex in-site link structures and login controls of most forum sites. Without carefully selecting the traversal path, a generic crawler usually downloads many duplicate and invalid pages from forums, and thus wastes both the precious bandwidth and the limited storage space. To crawl forum data more effectively and efficiently, in this paper, we propose an automatic approach to exploring an appropriate traversal strategy to direct the crawling of a given target forum. In detail, the traversal strategy consists of the identification of the skeleton links and the detection of the page-flipping links. The skeleton links instruct the crawler to only crawl valuable pages and meanwhile avoid duplicate and uninformative ones; and the page-flipping links tell the crawler how to completely download a long discussion thread which is usually shown in multiple pages in Web forums. The extensive experimental results on several forums show encouraging performance of our approach. Following the discovered traversal strategy, our forum crawler can archive more informative pages in comparison with previous related work and a commercial generic crawler.

We study in this paper the Web forum crawling problem, which is a very fundamental step in many Web applications, such as search engine and Web data mining. As a typical user-created content (UCC), Web forum has become an important resource on the Web due to its rich information contributed by millions of Internet users every day. However, Web forum crawling is not a trivial problem due to the in-depth link structures, the large amount of duplicate pages, as well as many invalid pages caused by login failure issues. In this paper, we propose and build a prototype of an intelligent forum crawler, iRobot, which has intelligence to understand the content and the structure of a forum site, and then decide how to choose traversal paths among different kinds of pages. To do this, we first randomly sample (download) a few pages from the target forum site, and introduce the page content layout as the characteristics to group those pre-sampled pages and re-construct the forum's sitemap. After that, we select an optimal crawling path which only traverses informative pages and skips invalid and duplicate ones. The extensive experimental results on several forums show the performance of our system in the following aspects: 1) Effectiveness - Compared to a generic crawler, iRobot significantly decreases the duplicate and invalid pages; 2) Efficiency - With a small cost of pre-sampling a few pages for learning the necessary knowledge, iRobot saves substantial network bandwidth and storage as it only fetches informative pages from a forum site; and 3) Long threads that are divided into multiple pages can be re-concatenated and archived as a whole thread, which is of great help for further indexing and data mining.

Online collaboration and sharing is the central theme of many web-based services that create the so-called Web 2.0 phenomena. Using the Internet as a computing platform, many Web 2.0 applications set up mirror sites to provide large-scale availability and to achieve load balance. However, in the age of Web 2.0, where every user is also a writer and publisher, the deployment of mirror sites makes consistency maintenance a Web scale problem. Traditional concurrency control methods (e.g. two phase lock, serialization, etc.) are not up to the task for several reasons. First, large network latency between mirror sites will make two phase locking a throughput bottleneck. Second, locking will block a large portion of concurrent operations, which makes it impossible to provide large-scale availability. On the other hand, most Web 2.0 operations do not need strict serializability - it is not the intention of a user who is correcting a typo in a shared document to block another who is adding a comment, as long as consistency can still be achieved. Thus, in order to enable maximal online collaboration and sharing, we need a lock-free mechanism that can maintain consistency among mirror sites on the Web. In this paper, we propose a flexible and efficient method to achieve consistency maintenance in the Web 2.0 world. Our experiments show its good performance improvement compared with existing methods based on distributed lock.

Recent studies have indicated the significance of supporting real-time group editing in "Wiki" applications, whose collaboration environments have their dynamic and large-scale nature. Correct capture of causal relationships between operations from different users is crucial in order to preserve consistency of object copies. This challenge was resolved by employing vector logical clock. But since its size is equal to the number of cooperating sites, it has low efficiency when dealing with a collaborative environment involving a large number of participants. In this paper, we propose a direct causal vector (DCV) approach for solving causality detection issues in real-time group editors. DCV timestamp does not record the causality information that can be deduced from the transitivity of causal relation. As a result, it can automatically reduce its own size when people leave the collaboration session and always keep small. We prove that DCV approach is well fit for capturing causality in wiki like large-scale dynamic collaboration environments.

The dynamic, heterogeneous and autonomous nature of grid environment brings some new characteristics to grid workflow, which makes it quite different from the traditional workflow. Therefore, the uncertainty and complexity of the working processes are increasing. A norm-driven state machine model of grid workflow named GridSM is offered, which utilizes norm to describe the grid workflow systems. Its advantages are that norm can express different semantics exactly and suits perfectly for depicting complicated systems. The ratiocinative ability of norm can verify the norm description of grid workflow systems, check the semantic conflicts among the norms, insure the completeness of the norm description and reflect the dynamic changes of the working processes. GridSM can also picture real-time states of grid workflow systems, which will be the theoretical foundation for the simulation of systems. At last, the prototype system named NormTools verifies the norm description of grid sorting flow, and the result is satisfying

Replicated architecture is widely used for concealing network delay. However, consistency maintenance in fully replicated architecture is a major technical challenge. In this paper, we report a Mark & Retrace based method in replicated groupware systems. Compared with the Operation Transformation technique, it does not adjust the operation's position but retraces the document's address space to the state at the time of the operation's generation. Then the operation can be executed directly in this address space. Mark & Retrace method can not only achieve the same goal of consistency maintenance but also provide a better support for Undo. This paper provides the proof of the algorithm's correctness of consistency maintenance, in which both the orders of character nodes and marks of each node at all sites are kept consistent. Furthermore, the amortized efficiency can reach O(log n).