Interactive Construction of User-Centric Dictionary for Text Analytics

We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine, which helps the creation of flexible dictionaries with precise granularity required in typical text analysis. This paper introduces the first formulation of interactive dictionary construction to address this issue. To optimize the interaction, we propose a new algorithm that effectively captures an analyst’s intention starting from only a small number of sample terms. Along with the algorithm, we also design an automatic evaluation framework that provides a systematic assessment of any interactive method for the dictionary creation task. Experiments using real scenario based corpora and dictionaries show that our algorithm outperforms baseline methods, and works even with a small number of interactions.

[1]  Daniel Gruhl,et al.  Interactive Dictionary Expansion using Neural Language Models , 2018, HumL@ISWC.

[2]  Mohamed M. Mostafa,et al.  More than words: Social networks' text mining for consumer brand sentiments , 2013, Expert Syst. Appl..

[3]  Eric Crestan,et al.  Web-Scale Distributional Similarity and Entity Set Expansion , 2009, EMNLP.

[4]  Jason Weston,et al.  Dialogue Learning With Human-In-The-Loop , 2016, ICLR.

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

[6]  Yeye He,et al.  SEISA: set expansion by iterative similarity aggregation , 2011, WWW.

[7]  Shourya Roy,et al.  Getting insights from the voices of customers: Conversation mining at a contact center , 2009, Inf. Sci..

[8]  Feng Zhou,et al.  Fine-Grained Categorization and Dataset Bootstrapping Using Deep Metric Learning with Humans in the Loop , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Tetsuya Nasukawa,et al.  Text analysis and knowledge mining system , 2001, IBM Syst. J..

[10]  Anna Lisa Gentile,et al.  Multi-lingual Concept Extraction with Linked Data and Human-in-the-Loop , 2017, K-CAP.

[11]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[12]  Brian M. Sadler,et al.  HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion , 2018, KDD.

[13]  Patrick Pantel,et al.  Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations , 2006, ACL.

[14]  Jiawei Han,et al.  SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble , 2017, ECML/PKDD.

[15]  Omer Levy,et al.  Improving Distributional Similarity with Lessons Learned from Word Embeddings , 2015, TACL.

[16]  Neal Lewis,et al.  SPOT the Drug! An Unsupervised Pattern Matching Method to Extract Drug Names from Very Large Clinical Corpora , 2012, 2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology.

[17]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[18]  Ashish Verma,et al.  Building re-usable dictionary repositories for real-world text mining , 2010, CIKM '10.

[19]  Tetsuya Nasukawa.  Text analysis and knowledge mining , 2009, 2009 Eighth International Symposium on Natural Language Processing.

[20]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[21]  Greg Schohn,et al.  Less is More: Active Learning with Support Vector Machines , 2000, ICML.

[22]  Bryan Pardo,et al.  A Human-in-the-Loop System for Sound Event Detection and Annotation , 2018, ACM Trans. Interact. Intell. Syst..