Authors: Anastasia Zhukova, Felix Hamborg, Bela Gipp Publication date: 2020/08/05 Journal: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL) Abstract: Dataset exploration is a set of techniques crucial in many research and data science projects. For textual datasets, commonly used techniques include topic modeling, document summarization, and methods related to dimension reduction. Despite their robustness, these techniques suffer from at least one of the following drawbacks: document summarization does not explicitly set documents in relation, the others yield summaries or topics that often are difficult to interpret and yield poor results for topics that consist of context-dependent terms. We propose a method for dataset exploration that employs cross-document near-identity resolution of mentions of semantic concepts, such as persons, other named entity types, events, actions. The method not only sets documents in relation and thus allows for comparative dataset exploration, but also yields well interpretable document representations. Additionally, due to the underlying approach for cross-document resolution of concept mentions, the method is able to set documents in relation as to their near-identity terms, e.g., synonyms that are not universally valid but only in the given dataset. PDF: https://dl.acm.org/doi/pdf/10.1145/3383583.3398562

1. Interpretable and Comparative Textual Dataset
Exploration Using Near-Identity Mention Relations
Anastasia Zhukova1, Felix Hamborg2, Bela Gipp1
Problem
• Text dataset exploration lacks of methods
allow text comparison and content
interpretation
• SoTA: uni-/bigram instead of phrase level
• Lack of identification of complex
coreferential relations
2. Detailed representation
On cluster level: NI-CDCR (near-identity cross-document coreference resolution)
Contact
Web: https://dke.uni-wuppertal.de/en/
Email: zhukova@uni-wuppertal.de, felix.hamborg@uni.kn
1 University of Wuppertal, 2 University of Konstanz
Research question
How to explore text datasets to ensure
comparability across texts, interpretability of
representation, and employment of the
complex semantic relations among text
components?
Features
• Histogram-matrix → comparative text
exploration
• Dimensions
o LDA-based clusters
o user-defined metadata / extracted
information
• Resolution of semantically complex and
lexical diverse concepts (NI-CDCR)
o Identity: “Cuba” = “the Caribbean Pearl”
o Near-identity: “US government” ~ “Trump
Administration” ~ “White House”
o Context identity: “resuming its nuclear
program” = “Tehran's nuclear ambitions”
• Easy-to-interpret document representation
• Three levels of analysis: cluster, detailed,
and document view
• User-controlled number of concepts
Metadata:
news
slant
/
frame
/
subtopic
0+2 5 6 7 3 1 4+9
8
RR
R
M
LL
L
0 2
Document view
Metadata: event id
Detailed view
Methodology
1. High-level representation
On article level: LDA
• cosine similarity
between LDA-topic
vectors
• small squares: articles
• 𝑠𝑖𝑚 ≥ 0.7 → related
0
1
2
3
4
5
6
7
8
9
4+9
4+9
0+2
0+2
Event ids
“Trump” entity is mentioned across all clusters
253
Cluster view