Question: What do the authors consider to illustrate that semantically different yet related applications may have significant potential for the reuse of features?
Evidence: To illustrate that semantically different yet related applications may have significant potential for reuse of features, let us consider the problem of event extraction, which involves identifying occurrences of specific type of events or activities from raw text.
Towards that, we analysed published works on three different types of events in different domains as described next:
Objective of this study is to design a ML model for identifying if there exist mentions of one of the nine types of bio-molecular interactions in (publicly available) Biomedical data. To train SVM based classifier, authors use GENETAG database, which is a tagged corpus for gene/protein named entity recognition. BioNLP 2009 shared task test-set was used to estimate performance of the system. Further details can be found at BIBREF22.
Objective of the study was to design ML model for enabling automated detection of specific financial events in the news text. Ten diﬀerent types of ﬁnancial events were considered including announcements regarding CEOs, presidents, products, competitors, partners, subsidiaries, share values, revenues, proﬁts, and losses. To train and test SVM and CRF based ML models, authors used data set consisting of 200 news messages extracted from the Yahoo! Business and Technology newsfeeds, having financial events and relations manually annotated by 3 domain experts. Further details can be found at BIBREF23.
Objective of the study was to design an ML based system for extracting open domain calendar of significant events from Twitter-data. 38 different types of events were considered for designing the system. To train the ML model, an annotated corpus of 1000 tweets (containing 19,484 tokens) was used and trained model was tested on 100 million most recent tweets. Further details can be found at BIBREF24.
Table TABREF21 below depicts classes of features selected by authors of these works (as described in the corresponding references above) to highlight the point that despite domain differences, these applications share similar sets of features. Since authors of these works did not cite each other, it is possible that that these features might have been identified independently. This, in turn, supports the hypothesis that if adequate details of any one or two of these applications are fed to a system described in this work, which is designed to estimate semantic similarities across applications, system can automatically suggest potential features for consideration for the remaining applications to start with without requiring manual knowledge of the semantically related applications.
To illustrate that semantically different yet related applications may have significant potential for reuse of features, let us consider the problem of event extraction, which involves identifying occurrences of specific type of events or activities from raw text.
Answer: The authors consider the problem of event extraction, which involves identifying occurrences of specific type of events or activities from raw text.