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Such so- lutions are either heuristic approaches or machine learn- ing based approaches with auto-generated training data. For example, Kong, Zhang, and Yu con- vert the attribute value text into bag-of-words vectors with TF-IDF weights, and then compute the similarity with the inner product and the cosine similarity. The process of sampling negative examples is not included in Algorithm 1.
Researchers have proposed unsupervised approaches for the UIL problem which requires no labelled data. The variations follow differ- ent patterns, such as acronyms, abbreviations, synonyms and translations, in different social networks. First, it automatically captures the word-level mapping and sequence-level mapping with almost no feature engineering; Second, it only requires positive examples aligned attribute pairs as training data which relaxes the effort of sampling negative examples.
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CoLink employs a co-training algorithm, which manipu- lates two independent models, the attribute-based model and the relationship-based model, and makes them reinforce each other iteratively in an unsupervised way. The performance very much depends on the quantity and quality of the auto-generated training data which is sensitive to the targeted UIL tasks.