ACRank: a multi-evidence text-mining model for alliance discovery from news articles

Author:

Zhou YiluORCID,Xue Yuan

Abstract

PurposeStrategic alliances among organizations are some of the central drivers of innovation and economic growth. However, the discovery of alliances has relied on pure manual search and has limited scope. This paper proposes a text-mining framework, ACRank, that automatically extracts alliances from news articles. ACRank aims to provide human analysts with a higher coverage of strategic alliances compared to existing databases, yet maintain a reasonable extraction precision. It has the potential to discover alliances involving less well-known companies, a situation often neglected by commercial databases.Design/methodology/approachThe proposed framework is a systematic process of alliance extraction and validation using natural language processing techniques and alliance domain knowledge. The process integrates news article search, entity extraction, and syntactic and semantic linguistic parsing techniques. In particular, Alliance Discovery Template (ADT) identifies a number of linguistic templates expanded from expert domain knowledge and extract potential alliances at sentence-level. Alliance Confidence Ranking (ACRank)further validates each unique alliance based on multiple features at document-level. The framework is designed to deal with extremely skewed, noisy data from news articles.FindingsIn evaluating the performance of ACRank on a gold standard data set of IBM alliances (2006–2008) showed that: Sentence-level ADT-based extraction achieved 78.1% recall and 44.7% precision and eliminated over 99% of the noise in news articles. ACRank further improved precision to 97% with the top20% of extracted alliance instances. Further comparison with Thomson Reuters SDC database showed that SDC covered less than 20% of total alliances, while ACRank covered 67%. When applying ACRank to Dow 30 company news articles, ACRank is estimated to achieve a recall between 0.48 and 0.95, and only 15% of the alliances appeared in SDC.Originality/valueThe research framework proposed in this paper indicates a promising direction of building a comprehensive alliance database using automatic approaches. It adds value to academic studies and business analyses that require in-depth knowledge of strategic alliances. It also encourages other innovative studies that use text mining and data analytics to study business relations.

Publisher

Emerald

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Reference48 articles.

1. A unified model for metasearch, pooling, and system evaluation,2003

2. A statistical method for system evaluation using incomplete judgments,2006

3. Banko, M. and Etzioni, O. (2008), “The tradeoffs between open and traditional relation extraction”, Proceedings of the Association for Computational Linguistics (ACL) -08: Human Language Technology Conference (HLT), Columbus, OH, pp. 28-36.

4. Competitor mining with the web;IEEE Transactions on Knowledge and Data Engineering,2008

5. Understanding business ecosystem dynamics: a data-driven approach;ACM Transactions on Management Information Systems (TMIS),2015

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