Improving Human Needs Categorization of Events with Semantic Classification

Human Needs categories have been used to characterize the reason why an affective event is positive or negative. For example, “I got the flu” and “I got fired” are both negative (undesirable) events, but getting the flu is a Health problem while getting fired is a Financial problem. Previous work created learning models to assign events to Human Needs categories based on their words and contexts. In this paper, we introduce an intermediate step that assigns words to relevant semantic concepts. We create lightly supervised models that learn to label words with respect to 10 semantic concepts associated with Human Needs categories, and incorporate these labels as features for event categorization. Our results show that recognizing relevant semantic concepts improves both the recall and precision of Human Needs categorization for events.

[1]  A. Maslow.  Motivation and Personality , 1954 .

[2]  Manfred A. Max-Neef.  Human Scale Development: Conception, Application and Further Reflections , 1989 .

[3]  Eduard H. Hovy,et al.  Fine Grained Classification of Named Entities , 2002, COLING.

[4]  Ellen Riloff,et al.  A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts , 2002, EMNLP.

[5]  James W. Pennebaker,et al.  Linguistic Inquiry and Word Count (LIWC2007) , 2007 .

[6]  James R. Curran,et al.  Reducing Semantic Drift with Bagging and Distributional Similarity , 2009, ACL.

[7]  Ellen Riloff,et al.  Inducing Domain-Specific Semantic Class Taggers from (Almost) Nothing , 2010, ACL.

[8]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[9]  Daniel S. Weld,et al.  Fine-Grained Entity Recognition , 2012, AAAI.

[10]  Ellen Riloff,et al.  Ensemble-based Semantic Lexicon Induction for Semantic Tagging , 2012, *SEMEVAL.

[11]  Janyce Wiebe,et al.  Benefactive/Malefactive Event and Writer Attitude Annotation , 2013, ACL.

[12]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[13]  Stefano Faralli,et al.  GlossBoot: Bootstrapping Multilingual Domain Glossaries from the Web , 2013, ACL.

[14]  Ellen Riloff,et al.  A COMPUTATIONAL MODEL FOR PLOT UNITS , 2013, Comput. Intell..

[15]  Janyce Wiebe,et al.  Sentiment Propagation via Implicature Constraints , 2014, EACL.

[16]  Tomoki Toda,et al.  Acquiring a Dictionary of Emotion-Provoking Events , 2014, EACL.

[17]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[18]  Claire Cardie,et al.  Major Life Event Extraction from Twitter based on Congratulations/Condolences Speech Acts , 2014, EMNLP.

[19]  Christopher D. Manning,et al.  Distributed Representations of Words to Guide Bootstrapped Entity Classifiers , 2015, NAACL.

[20]  Ellen Riloff,et al.  Acquiring Knowledge of Affective Events from Blogs Using Label Propagation , 2016, AAAI.

[21]  Marilyn A. Walker,et al.  Learning Lexico-Functional Patterns for First-Person Affect , 2017, ACL.

[22]  Richard S. Zemel,et al.  Prototypical Networks for Few-shot Learning , 2017, NIPS.

[23]  Ellen Riloff,et al.  Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data , 2018, NAACL.

[24]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[25]  William A. Sethares,et al.  Domain Adapted Word Embeddings for Improved Sentiment Classification , 2018, ACL.

[26]  Ellen Riloff,et al.  Weakly Supervised Induction of Affective Events by Optimizing Semantic Consistency , 2018, AAAI.

[27]  Ellen Riloff,et al.  Why Is an Event Affective? Classifying Affective Events Based on Human Needs , 2018, AAAI Workshops.

[28]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.