---
res:
  bibo_abstract:
  - This paper deals with aspect phrase extraction and classification in sentiment
    analysis. We summarize current approaches and datasets from the domain of aspect-based
    sentiment analysis. This domain detects sentiments expressed for individual aspects
    in unstructured text data. So far, mainly commercial user reviews for products
    or services such as restaurants were investigated. We here present our dataset
    consisting of German physician reviews, a sensitive and linguistically complex
    field. Furthermore, we describe the annotation process of a dataset for supervised
    learning with neural networks. Moreover, we introduce our model for extracting
    and classifying aspect phrases in one step, which obtains an F1-score of 80%.
    By applying it to a more complex domain, our approach and results outperform previous
    approaches.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Joschka
      foaf_name: Kersting, Joschka
      foaf_surname: Kersting
      foaf_workInfoHomepage: http://www.librecat.org/personId=58701
  - foaf_Person:
      foaf_givenName: Michaela
      foaf_name: Geierhos, Michaela
      foaf_surname: Geierhos
      foaf_workInfoHomepage: http://www.librecat.org/personId=42496
    orcid: 0000-0002-8180-5606
  dct_date: 2020^xs_gYear
  dct_language: eng
  dct_publisher: SCITEPRESS@
  dct_subject:
  - Deep Learning
  - Natural Language Processing
  - Aspect-based Sentiment Analysis
  dct_title: Aspect Phrase Extraction in Sentiment Analysis with Deep Learning@
...