Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction

Stefan Falk; Andi Rexha; Roman Kern

doi:10.18653/v1/s16-1042

Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction

Stefan Falk
, Andi Rexha
, Roman Kern

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

This paper describes our participation in SemEval-2016 Task 5 for Subtask 1, Slot 2. The challenge demands to find domain specific target expressions on sentence level that refer to reviewed entities. The detection of target words is achieved by using word vectors and their grammatical dependency relationships to classify each word in a sentence into target or non-target. A heuristic based function then expands the classified target words to the whole target phrase. Our system achieved an F1 score of 56.816% for this task. © 2016 Association for Computational Linguistics.

Original language	German
Title of host publication	Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
DOIs	https://doi.org/10.18653/v1/s16-1042
Publication status	Published - 2016
Event	10th International Workshop on Semantic Evaluation: SemEval 2016 - San Diego, United States Duration: 16 Jun 2016 → 17 Jun 2016

Conference

Conference	10th International Workshop on Semantic Evaluation
Country/Territory	United States
City	San Diego
Period	16/06/16 → 17/06/16

Fields of Expertise

Information, Communication & Computing

Access to Document

10.18653/v1/s16-1042Licence: CC BY 4.0

Cite this

@inproceedings{e454834f75bf4324ad37936a9eac64d9,

title = "Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction",

abstract = "This paper describes our participation in SemEval-2016 Task 5 for Subtask 1, Slot 2. The challenge demands to find domain specific target expressions on sentence level that refer to reviewed entities. The detection of target words is achieved by using word vectors and their grammatical dependency relationships to classify each word in a sentence into target or non-target. A heuristic based function then expands the classified target words to the whole target phrase. Our system achieved an F1 score of 56.816\% for this task. {\textcopyright} 2016 Association for Computational Linguistics.",

author = "Stefan Falk and Andi Rexha and Roman Kern",

year = "2016",

doi = "10.18653/v1/s16-1042",

language = "deutsch",

booktitle = "Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)",

note = "10th International Workshop on Semantic Evaluation : SemEval 2016 ; Conference date: 16-06-2016 Through 17-06-2016",

}

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AU - Falk, Stefan

AU - Rexha, Andi

AU - Kern, Roman

PY - 2016

Y1 - 2016

N2 - This paper describes our participation in SemEval-2016 Task 5 for Subtask 1, Slot 2. The challenge demands to find domain specific target expressions on sentence level that refer to reviewed entities. The detection of target words is achieved by using word vectors and their grammatical dependency relationships to classify each word in a sentence into target or non-target. A heuristic based function then expands the classified target words to the whole target phrase. Our system achieved an F1 score of 56.816% for this task. © 2016 Association for Computational Linguistics.

AB - This paper describes our participation in SemEval-2016 Task 5 for Subtask 1, Slot 2. The challenge demands to find domain specific target expressions on sentence level that refer to reviewed entities. The detection of target words is achieved by using word vectors and their grammatical dependency relationships to classify each word in a sentence into target or non-target. A heuristic based function then expands the classified target words to the whole target phrase. Our system achieved an F1 score of 56.816% for this task. © 2016 Association for Computational Linguistics.

U2 - 10.18653/v1/s16-1042

DO - 10.18653/v1/s16-1042

M3 - Beitrag in einem Konferenzband

BT - Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

T2 - 10th International Workshop on Semantic Evaluation

Y2 - 16 June 2016 through 17 June 2016

ER -