Competitor identification tries to find competitors of some entity in a given field, which is the key to the success of market intelligence. Manually collecting competitors is labor-intensive and time consuming. So automatic approaches are proposed for this purpose. However, these approaches suffer from the following two main challenges. Competitor information might not only be contained in semi-structured sources like lists or tables, but also be mentioned in free texts. The diversity of its sources make competitor identification quite difficult. Also, these competitors might not always occur in form of their full names. The occurrences of name variants further increase the diversity, and make the task more challenging. In this paper, we propose a novel unsupervised approach to identify competitors from prospectuses based on a multi-strategy learning algorithm. More precisely, we first extract competitors from lists using some predefined heuristic rules. By leveraging redundancies among competitor information in lists, tables, and texts, these competitors are fed as seeds to distantly supervise the learning process to find table columns and text patterns containing competitors. The whole process is iteratively performed. In each iteration, the newly discovered competitors of high confidence from various sources are treated as new seeds for bootstrapping. The experimental results show the effectiveness of our approach without human intentions and external knowledge bases. Moreover, the approach significantly outperforms traditional named entity recognition approaches.

A Multi-strategy Learning Approach to Competitor Identification

Entity Linking is widely used in entity retrieval and semantic search. It refers mentions in unstructured documents to their representations in a knowledge base (KB). The frequently used KB (e.g. Wikipedia) usually contains abundant information corresponding to each entity, such as properties, name variations and text descriptions, which can help to find candidates and disambiguate the links. In this paper, we link organization names in Chinese documents to a list-like KB. Compared to typical KBs, the records in our KB are simply Chinese organization full names. The massive variations, or abbreviations in the documents cannot be directly matched to any organization name in the KB and bring about ambiguities, thus make the linking task difficult. At first, we enrich the KB with the abbreviations. Making use of the information from Hudong Baike and other sources, we design a pattern based full name annotation method to help generate abbreviations for all the names in the KB. To resolve the ambiguity problem, we propose a two-stage linking generation approach utilizing the co-occurrence of abbreviations and full names in the same document or document cluster, where the linked full names in the first stage constraint the linking of abbreviations in the second stage. We apply our approach to police inquiry document corpus. The experiment results show the effectiveness of our approach and outperforms the one-stage approach significantly in terms of precision and recall.

Effective Chinese Organization Name Linking to a List-Like Knowledge Base

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Effective Chinese Organization Name Linking to a List-Like Knowledge Base