Noise Mitigation for Neural Entity Typing and Relation Extraction
Table 3: P@1, Micro F1 for all, head and tail entities and MAP results for entity typing.
['[EMPTY]', '[EMPTY]', '[ITALIC] P@1 all', '[ITALIC] F1 all', '[ITALIC] F1 head', '[ITALIC] F1 tail', 'MAP']
EntEmb and FIGMENT baselines. Following \newcitefigment15, we also learn entity embeddings and classify those embeddings to types, i.e., instead of distant supervision, we classify entities based on aggregated information represented in entity embeddings. An MLP with one hidden layer is used as classifier. We call that model EntEmb. Results. For F1, we report separate results for all, head (frequency higher than 100) and tail (frequency less than 5) entities. Analysis of joint training. In this paragraph, we investigate the joint training in more detail. In particular, we evaluate different variants of it by combining relation extraction with other entity typing approaches: EntEmb and FIGMENT. For joint training with ET-EntEmb, we do not use the context for predicting the types of the relation arguments but only their embeddings. Then, we feed those embeddings into an MLP which computes a representation that we use for the type prediction. For joint training with ET-FIGMENT, we compute two different cost functions for entity typing: one for typing based on entity embeddings (see ET-EntEmb above) and one for typing based on an MLP context model. In addition to those two baseline ET models, we also train a version in which both entity typing and relation extraction use EntEmb as their only input features. The curve for the model that uses only entity embedding features for both entity typing and relation extraction is much worse than the other curves. Similarly, the curve for the model with EntEmb as entity typing component has more precision variations than the curves for the other models which use context features for ET. Thus, joint training does not help per se but it is important which models are trained together. The areas under the PR curves show the following model trends: joint with ET-FIGMENT ≈ joint as in Figure 1 > joint with ET-EntEmb > joint with ET-EntEmb and RE-EntEmb.