Paper Study

Hierarchical Neural Autoencoders

Introduction

LSTM captures local compositions (the way neighboring words are combined semantically and syntatically

Paragraph Autoencoder Models

Model 1: Standard LSTM

Model 2: Hierarchical LSTM

Model 3: Hierarchical LSTM with Attention

Evaluation - Summarization

• ROUGE: recall-oriented score
• BLEU: precision-oriented score

Task 5:

Aspect Based Sentiment Analysis (ABSA)

Subtask 1: Sentence-level ABSA

Given a review text about a target entity (laptop, restaurant, etc.),

identify the following information:

• Slot 1: Aspect Category
  • ​ex. ''It is extremely portable and easily connects to WIFI at the library and elsewhere''
    ----->{LAPTOP#PORTABILITY}, {LAPTOP#CONNECTIVITY}
• Slot 2: Opinion Target Expression (OTE)
  • ​an expression used in the given text to refer to the reviewed E#A
  • ​ex. ''The fajitas were delicious, but expensive''
    ----->{FOOD#QUALITY, “fajitas”}, {FOOD#PRICES, “fajitas”}
• Slot 3: Sentiment Polarity
  • ​label: (positive, negative, or neutral) 

Subtask 2: Text-level ABSA

Given a set of customer reviews about a target entity (ex. a restaurant), identify a set of {aspect, polarity} tuples that summarize the opinions expressed in each review.

Subtask 3: Out-of-domain ABSA

Test system in a previously unseen domain (hotel reviews in SemEval 2015) for which no training data was made available. The gold annotations for Slots 1 and 2 were provided and the teams had to return the sentiment polarity values (Slot 3).

Our Framework

Framework 1

Encode by Tree-LSTM or autoencoder

Framework 2

End-to-end MemNN