Relation Extraction

Sentence-level Relation Extraction: classify a relation mention into a set of relation types of interest or Not-Target-Type (None)

Relation Mention

an entity pair with a sentence / context.
E.g., ("Hussein", "Amman'', Hussein was born in Amman on 14 November 1935.)

Relation Types of Interest

A set of relation types.
E.g., {Born-in, President-of, Died-in, Parents-of, ...}

Relation Extraction

predict Born-in for ("Hussein", "Amman'', Hussein was born in Amman on 14 November 1935.)

Distant Supervision

Automatically generate annotations by Knowledge Base (KB).

For example, because Born-in(Obama, USA) and President-of(Obama, USA) and Citizen-of(Obama, USA) exists in KB, ("Obama", "USA", Obama was born in Honolulu, Hawaii, USA as he has always said) would be annotated as Born-in (correct) and President-of (wrong).

Heterogeneous Supervision

Distant supervision only encodes KB, while we have more than KB.

• Knowledge Base:
  • return Born_in for (e1, e2, s) if Born_in(e1, e2) in KB
  • return Died_in for (e1, e2, s) if Died_in(e1, e2) in KB
• Heuristic Patterns:
  • return Born_in for (e1, e2, s) if match('* born in *', s)
  • return Died_in for (e1, e2, s) if match('* died in *', s)

Heterogeneous Supervision encodes heterogeneous supervision by means of labeling functions.

Challenges

• Original Relation Extraction Task
• True Label Discovery: Resolves conflicts among Heterogeneous Supervision

Our Solution

Intuition

• Relation Extraction: Matching appropriate relation type to context.
  • ("Obama", "USA", Obama was born in Honolulu, Hawaii, USA as he has always said) should not be categorized as President-of, since the context does not match.
• True Label Discovery: Finding most reliable annotation w.r.t. context.
  • A labeling function could be more reliable for a subset of instances comparing to the rest.

Since Context plays an important role in both tasks, we employed Representation Learning to capture context information, and bridges these two tasks.

A Representation Learning Approach

Experiments

Labeling Function

• Knowledge Base: directly adopting annotations of Distant Supervision.
• Heuristic Patterns: human constructed by analysis of Annotations generated by KB

True Label Discovery

In Table 1, the first two relation mentions come from Wiki-KBP, and their annotations are {born-in, None}. The last two are created by replacing key words of the first two. Key words are marked as bold and entity mentions are marked as Italics.

Investment and Universal Schemas refer None as true type for all four instances in Table 1. And our method infers born-in as the true label for the first two relation mentions. After replacing the matched contexts (born) with other words (elected and examined), our method no longer trusts born-in since the modified contexts are no longer matched, then infers None as the true label. In other words, our proposed method infer the true label in a context aware manner.

Relation Extraction

Resources

Softwares and labeling functions have been uploaded to Github

Reference

Please cite the following paper if you find the codes and datasets useful:

@inproceedings{Liu2017rehession,
  title={Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach},
  author={Liu, Liyuan and Ren, Xiang and Zhu, Qi and Zhi, Shi and Gui, Huan and Ji, Heng and Han, Jiawei},
  booktitle={Proc. EMNLP},
  year={2017}
}