Sentence Entity Mention Classification Task

A Sentence Entity Mention Classification Task is a Multilabel Classification Task that requires the Entity Mentions (from an Entity Mention Set) within the Sentences of a Text Document.

• AKA: Entity Assignment Task.
• Context:
  • Input:
    • an Entity Mention Set.
    • a Linguistic Expression Sequence.
  • output: zero or more Entity Identifiers for each Linguistic Sentence.
• Example(s):
  • [math]\displaystyle{ f }[/math]({Camera-A, Camera-B}, "I bought Camera-A yesterday. I took some pictures in the evening in my living room. The images are very clear. They are definitely better than those from my old Camera-B. The battery is very good too.”)
    • Sentence 1: {Camera-A}
    • Sentence 2: {Camera-A}
    • Sentence 3: {Camera-A}
    • Sentence 4: {Camera-A, Camera-B}
    • Sentence 5: {Camera-A}
• See: Document Entity Mention Classification Task.

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References

2009

• (Ding et al., 2009) ⇒ Xiaowen Ding, Bing Liu, and Lei Zhang. (2009). “Entity Discovery and Assignment for Opinion Mining Applications.” In: Proceedings of ACM SIGKDD Conference (KDD-2009). doi:10.1145/1557019.1557141
  • Opinion mining became an important topic of study in recent years due to its wide range of applications. There are also many companies offering opinion mining services. One problem that has not been studied so far is the assignment of entities that have been talked about in each sentence.
  • Problem statement: Given a set of threads T in a particular domain, two tasks are performed in this paper:
    • 1. Entity discovery: discover the set of entities $E$ discussed in the posts of the threads, and
    • 2. Entity assignment: assign the entities in $E$ that each sentence si of each post p_j in [math]\displaystyle{ t }[/math] (In T) talks about.

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