• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.131-150
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• 2015

Omission of noun phrases for obligatory cases is a common phenomenon in sentences of Korean and Japanese, which is not observed in English. When an argument of a predicate can be filled with a noun phrase co-referential with the title, the argument is more easily omitted in Encyclopedia texts. The omitted noun phrase is called a zero anaphor or zero pronoun. Encyclopedias like Wikipedia are major source for information extraction by intelligent application systems such as information retrieval and question answering systems. However, omission of noun phrases makes the quality of information extraction poor. This paper deals with the problem of developing a system that can restore omitted noun phrases in encyclopedia documents. The problem that our system deals with is almost similar to zero anaphora resolution which is one of the important problems in natural language processing. A noun phrase existing in the text that can be used for restoration is called an antecedent. An antecedent must be co-referential with the zero anaphor. While the candidates for the antecedent are only noun phrases in the same text in case of zero anaphora resolution, the title is also a candidate in our problem. In our system, the first stage is in charge of detecting the zero anaphor. In the second stage, antecedent search is carried out by considering the candidates. If antecedent search fails, an attempt made, in the third stage, to use the title as the antecedent. The main characteristic of our system is to make use of a structural SVM for finding the antecedent. The noun phrases in the text that appear before the position of zero anaphor comprise the search space. The main technique used in the methods proposed in previous research works is to perform binary classification for all the noun phrases in the search space. The noun phrase classified to be an antecedent with highest confidence is selected as the antecedent. However, we propose in this paper that antecedent search is viewed as the problem of assigning the antecedent indicator labels to a sequence of noun phrases. In other words, sequence labeling is employed in antecedent search in the text. We are the first to suggest this idea. To perform sequence labeling, we suggest to use a structural SVM which receives a sequence of noun phrases as input and returns the sequence of labels as output. An output label takes one of two values: one indicating that the corresponding noun phrase is the antecedent and the other indicating that it is not. The structural SVM we used is based on the modified Pegasos algorithm which exploits a subgradient descent methodology used for optimization problems. To train and test our system we selected a set of Wikipedia texts and constructed the annotated corpus in which gold-standard answers are provided such as zero anaphors and their possible antecedents. Training examples are prepared using the annotated corpus and used to train the SVMs and test the system. For zero anaphor detection, sentences are parsed by a syntactic analyzer and subject or object cases omitted are identified. Thus performance of our system is dependent on that of the syntactic analyzer, which is a limitation of our system. When an antecedent is not found in the text, our system tries to use the title to restore the zero anaphor. This is based on binary classification using the regular SVM. The experiment showed that our system's performance is F1 = 68.58%. This means that state-of-the-art system can be developed with our technique. It is expected that future work that enables the system to utilize semantic information can lead to a significant performance improvement.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.123-138
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• 2016

This study investigates intents of acquisitions in information technology industries. Mergers and acquisitions are a strategic decision at corporate-level and have been an important tool for a firm to grow. Plenty of firms in information technology industries have acquired startups to increase production efficiency, expand customer base, or improve quality over the last decades. For example, Google has made about 200 acquisitions since 2001, Cisco has acquired about 210 firms since 1993, Oracle has made about 125 acquisitions since 1994, and Microsoft has acquired about 200 firms since 1987. Although there have been many existing papers that theoretically study intents or motivations of acquisitions, there are limited papers that empirically investigate them mainly because it is challenging to measure and quantify intents of M&As. This study examines the intent of acquisitions by measuring specific intents for M&A transactions. Using our measures of acquisition intents, we compare the intents by four acquisition types: (1) the acquisition where a hardware firm acquires a hardware firm, (2) the acquisition where a hardware firm acquires a software/IT service firm, (3) the acquisition where a software/IT service firm acquires a hardware firm, and (4) the acquisition where a software /IT service firm acquires a software/IT service firm. We presume that there are difference in reasons why a hardware firm acquires another hardware firm, why a hardware firm acquires a software firm, why a software/IT service firm acquires a hardware firm, and why a software/IT service firm acquires another software/IT service firm. Using data of the M&As in US IT industries, we identified major intents of the M&As. The acquisition intents are identified based on the press release of M&A announcements and measured with four categories. First, an acquirer may have intents of cost saving in operations by sharing common resources between the acquirer and the target. The cost saving can accrue from economies of scope and scale. Second, an acquirer may have intents of product enhancement/development. Knowledge and skills transferred from the target may enable the acquirer to enhance the product quality or to expand product lines. Third, an acquirer may have intents of gain additional customer base to expand the market, to penetrate the market, or to enter a foreign market. Fourth, a firm may acquire a target with intents of expanding customer channels. By complementing existing channel to the customer, the firm can increase its revenue. Our results show that acquirers have had intents of cost saving more in acquisitions between hardware companies than in acquisitions between software companies. Hardware firms are more likely to acquire with intents of product enhancement or development than software firms. Overall, the intent of product enhancement/development is the most frequent intent in all of the four acquisition types, and the intent of customer base expansion is the second. We also analyze our data with the classification of production-side intents and customer-side intents, which is based on activities of the value chain of a firm. Intents of cost saving operations and those of product enhancement/development can be viewed as production-side intents and intents of customer base expansion and those of expanding customer channels can be viewed as customer-side intents. Our analysis shows that the ratio between the number of customer-side intents and that of production-side intents is higher in acquisitions where a software firm is an acquirer than in the acquisitions where a hardware firm is an acquirer. This study can contribute to IS literature. First, this study provides insights in understanding M&As in IT industries by answering for question of why an IT firm intends to another IT firm. Second, this study also provides distribution of acquisition intents for acquisition types.