OpenTag: Open Attribute Value Extraction from Product Profiles
Table 4. Performance comparison of different models on attribute value extraction for different product profiles and datasets. OpenTag outperforms other state-of-the-art NER systems (Kozareva et al., 2016; Huang et al., 2015; Lample et al., 2016; Ma and Hovy, 2016) based on BiLSTM-CRF.
['Datasets/Attribute', 'Models', 'Precision', 'Recall', 'Fscore']
Attribute value extraction results. We compare the performance of OpenTag with the aforementioned baselines for identifying attribute values from different product profiles (like title, description and bullets) and different sets of attributes (like brand, flavor and capacity) in different domains (like dog food, detergent and camera). The first column in the table shows the domain, profile type, and attribute we are interested in. We observe that OpenTag consistently outperforms competing methods with a high overall f-score of 82.8%. Joint extraction of multi-attribute values. In this experiment, we extract values for brand, flavor and capacity from titles of dogfood data jointly on a disjoint split of data. Using {B,I,O,E} as the tagging strategy, each attribute a has its own {Ba,Ia,Ea} tag with O shared among them – with a total of 10 tags for three attributes. OpenTag with tag flip sampling strategy for single attribute value extraction improves the precision from 59.5% (on our initial labeled set of 50 instances) to 91.7% and recall from 70.7% to 91.5%. Similar results hold true for multi-attribute value extraction.