Accurate Product Name Recognition from User Generated Content

Wu, Sen; Fang, Zhanpeng; Tang, Jie

doi:10.1109/icdmw.2012.129

Cited by 15 publications

(13 citation statements)

References 2 publications

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“…The main idea is to predict user interest toward a certain weibo based on their historical behavior records. For CRF, the code is mainly from Wu et al (). CRF+LBP means to apply LBP to calculate the expectation of CRF in each iteration to only incorporate direct influence into consideration, not considering indirect influence.…”

Section: Experiments Resultsmentioning

confidence: 99%

User‐level microblogging recommendation incorporating social influence

Luo

Ding

et al. 2016

Asso for Info Science & Tech

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Tencent Weibo, as one of the most popular micro-blogging services in China, has attracted millions of users, producing 30-60 millions of weibo (similar as tweet in Twitter) daily. With the overload problem of user generate content, Tencent users find it is more and more hard to browse and find valuable information at the first time. In this paper, we propose a Factor Graph based weibo recommendation algorithm TSI-WR (Topic-Level Social Influence based Weibo Recommendation), which could help Tencent users to find most suitable information. The main innovation is that we consider both direct and indirect social influence from topic level based on social balance theory. The main advantages of adopting this strategy are that it could first build a more accurate description of latent relationship between two users with weak connections, which could help to solve the data sparsity problem; second provide a more accurate recommendation for a certain user from a wider range. Other meaningful contextual information is also combined into our model, which include: Users profile, Users influence, Content of weibos, Topic information of weibos and etc. We also design a semi-supervised algorithm to further reduce the influence of data sparisty. The experiments show that all the selected variables are important and the proposed model outperforms several baseline methods.

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Section: Experiments Resultsmentioning

confidence: 99%

User‐level microblogging recommendation incorporating social influence

Luo

Ding

et al. 2016

Asso for Info Science & Tech

Self Cite

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“…Their method also links brand mentions to a catalog and discovers new brands. On the CPROD1 contest dataset [20], Wu et al [39] propose a hybrid framework for product mention recognition and linking to a product catalog with a large number of products. Yao and Sun [41] investigate mobile phone names extraction and normalization using a novel semi-supervised labeling scheme to generate training data at scale.…”

Section: Related Workmentioning

confidence: 99%

“…Existing information extraction applications on IT tickets mainly relies on text syntax and structure patterns, e.g., Part-of-Speech (POS) tags [1,28,37]. Studies on other domain-specific named entity extraction and linking problems mainly use local and contextual information, with limited external knowledge [30,36,39,41]. In this paper, we present a solution for extracting and linking software product names.…”

mentioning

confidence: 99%

Towards Effective Extraction and Linking of Software Mentions from User-Generated Support Tickets

Han

Goh

Sun

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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Software support tickets contain short and noisy text from users. Software products are represented by various surface forms and informal abbreviations created by users. Automatically identifying software mentions from tickets and determining the official names (and versions) are helpful for many downstream applications, e.g., routing the support tickets to the right team of experts supporting the software. In this work, we study the problem of software product name extraction and linking from support tickets. We first analyze a collection of annotated tickets to understand the language patterns. Second, we design features using multiple in-domain and web knowledge sources, for the extraction and linking with linear models. Experiments on four datasets show better and more consistent results of our methods compared to neural network baselines.

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“…Research on identification of product names has been performed in multiple domains, ranging from consumer electronics [6,9,10] to programming languages and libraries [5] to chemical compound names [2]. Extant research has primarily used supervised or semi-supervised methods.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised domain-agnostic identification of product names in social media posts

Pogrebnyakov

2018

2018 IEEE International Conference on Big Data (Big Data)

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Product name recognition is a significant practical problem, spurred by the greater availability of platforms for discussing products such as social media and product review functionalities of online marketplaces. Customers, product manufacturers and online marketplaces may want to identify product names in unstructured text to extract important insights, such as sentiment, surrounding a product. Much extant research on product name identification has been domain-specific (e.g., identifying mobile phone models) and used supervised or semisupervised methods. With massive numbers of new products released to the market every year such methods may require retraining on updated labeled data to stay relevant, and may transfer poorly across domains. This research addresses this challenge and develops a domain-agnostic, unsupervised algorithm for identifying product names based on Facebook posts. The algorithm consists of two general steps: (a) candidate product name identification using an off-the-shelf pretrained conditional random fields (CRF) model, part-of-speech tagging and a set of simple patterns; and (b) filtering of candidate names to remove spurious entries using clustering and word embeddings generated from the data.

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Accurate Product Name Recognition from User Generated Content

Cited by 15 publications

References 2 publications

User‐level microblogging recommendation incorporating social influence

User‐level microblogging recommendation incorporating social influence

Towards Effective Extraction and Linking of Software Mentions from User-Generated Support Tickets

Unsupervised domain-agnostic identification of product names in social media posts

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