Synthesis Lectures on Human Language Technologies

By Ido Dagan, Dan Roth, Mark Sammons, Fabio Massimo Zanzotto, Web Corpus Construction, Roland Schäfer, Felix Bildhauer. 2013

863 citations recorded by the source corpus.

Subfields: Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

Citations in the corpus

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021, background): “Yet, we are also different from Natural Language Inference (NLI) and textual entailment, which work with text directly, but cannot handle Horn rules (MacCartney and Manning, 2009; Dagan et al., 2013).”
Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science (2018, background): “The latter includes annotations for sentiment (Liu, 2012) and for figurative language or sarcasm (e.”
Attention-based LSTM for Aspect-level Sentiment Classification (2016, background): “Sentiment analysis (Nasukawa and Yi, 2003), also known as opinion mining (Liu, 2012), is a key NLP task that receives much attention these years.”
Document Modeling with Gated Recurrent Neural Network for Sentiment Classification (2015, background): “Document level sentiment classification is a fundamental task in sentiment analysis, and is crucial to understand user generated content in social networks or product reviews (Manning and Schütze, 1999; Jurafsky and Martin, 2000; Pang and Lee, 2008; Liu, 2012).”
Lifelong Learning for Sentiment Classification (2015, background): “Liu (2012) and Pang and Lee (2008) provided good surveys of the existing research.”
Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language (2015, background): “Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 643–653, Lisbon, Portugal, 17-21 September 2015. c©2015 Association for Computational Linguistics.”
Easy-First Dependency Parsing with Hierarchical Tree LSTMs (2016, background): “Dependency-based syntactic representations of sentences are central to many language processing tasks (Kübler et al., 2008).”
CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (2021, background): “In our case, a task is a separate aspect sentiment classification (ASC) problem of a product or domain (e.g., camera or phone) (Liu, 2012).”
Target-Dependent Sentiment Classification with Long Short Term Memory (2015, background): “Sentiment analysis, also known as opinion mining [Pang and Lee, 2008; Liu, 2012], is a fundamental task in natural language processing and computational linguistics [Manning and Schütze, 1999; Jurafsky and Martin, 2000].”
End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification (2017, background): “Sentiment classification is an important task in natural language processing and is essential to understand user opinions in social networks or product reviews [Pang and Lee, 2008; Liu, 2012].”
Using the Past Knowledge to Improve Sentiment Classification (2020, background): “Our work is related to sentiment classiﬁcation (Liu, 2012), lifelong learning and continual learning.”
Machine Learning Suites for Online Toxicity Detection (2018, methodology): “We also apply alternative crowd-sourced dictionaries such as Bing (Liu, 2012) and the NRC-Canada Word-Emotion Association Lexicon (Mohammed & Turney, 2013) with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy and disgust).”
Exploiting social relations for sentiment analysis in microblogging (2013, background): “Sentiment classification has been studied for years on various text corpus, like newspaper articles [33], movie reviews [31], and product reviews [5] [11] [23].”
Interactive Attention Networks for Aspect-Level Sentiment Classification (2017, background): “Aspect-level sentiment classification is a fine-grained task in sentiment analysis which aims to identify the sentiment polarity of targets in their context [Pang and Lee, 2008; Liu, 2012].”
Syntax-based Deep Matching of Short Texts (2015, methodology): “In both experiments we use precision at one (P@1) [Li, 2011] to measure the accuracy of matching.”