TY - JOUR
T1 - Topic modeling for sequential documents based on hybrid inter-document topic dependency
AU - Li, Wenbo
AU - Saigo, Hiroto
AU - Tong, Bin
AU - Suzuki, Einoshin
N1 - Funding Information:
A part of this research was supported by Grant-in-Aid for Scientific Research JP18H03290 from JSPS and the State Scholarship Fund 201706680067 from China Scholarship Council.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.
PY - 2021/6
Y1 - 2021/6
N2 - We propose two new topic modeling methods for sequential documents based on hybrid inter-document topic dependency. Topic modeling for sequential documents is the basis of many attractive applications such as emerging topic clustering and novel topic detection. For these tasks, most of the existing models introduce inter-document dependencies between topic distributions. However, in a real situation, adjacent emerging topics are often intertwined and mixed with outliers. These single-dependency based models have difficulties in handling the topic evolution in such multi-topic and outlier mixed sequential documents. To solve this problem, our first method considers three kinds of topic dependencies for each document to handle its probabilities of belonging to a fading topic, an emerging topic, or an independent topic. Secondly, we extend our first method by considering fine-grained dependencies in a given context for more complex topic evolution sequences. Our experiments conducted on six standard datasets on topic modeling show that our proposals outperform state-of-the-art models in terms of the accuracy of topic modeling, the quality of topic clustering, and the effectiveness of outlier detection.
AB - We propose two new topic modeling methods for sequential documents based on hybrid inter-document topic dependency. Topic modeling for sequential documents is the basis of many attractive applications such as emerging topic clustering and novel topic detection. For these tasks, most of the existing models introduce inter-document dependencies between topic distributions. However, in a real situation, adjacent emerging topics are often intertwined and mixed with outliers. These single-dependency based models have difficulties in handling the topic evolution in such multi-topic and outlier mixed sequential documents. To solve this problem, our first method considers three kinds of topic dependencies for each document to handle its probabilities of belonging to a fading topic, an emerging topic, or an independent topic. Secondly, we extend our first method by considering fine-grained dependencies in a given context for more complex topic evolution sequences. Our experiments conducted on six standard datasets on topic modeling show that our proposals outperform state-of-the-art models in terms of the accuracy of topic modeling, the quality of topic clustering, and the effectiveness of outlier detection.
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U2 - 10.1007/s10844-020-00635-4
DO - 10.1007/s10844-020-00635-4
M3 - Article
AN - SCOPUS:85099970240
VL - 56
SP - 435
EP - 458
JO - Journal of Intelligent Information Systems
JF - Journal of Intelligent Information Systems
SN - 0925-9902
IS - 3
ER -