Papers by Dan Moldovan
CEREC: A Corpus for Entity Resolution in Email Conversations (2020.coling-main)
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| Challenge: | e-mail corpus for entity resolution in email conversations is first large scale annotated corpus . ecc is a two-step process with minimal manual effort. |
| Approach: | They present the first large scale corpus for entity resolution in email conversations . they use 6001 email threads and 38,996 entity coreference chains to construct the corpus . |
| Outcome: | The proposed corpus is the first large scale annotated corpus for entity resolution in email conversations. |
Affect inTweets: A Transfer Learning Approach (2020.lrec-1)
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| Challenge: | Existing machine learning models require considerable effort to design task specific features to understand affectual states of people. |
| Approach: | They propose a transfer-learning based approach to infer the affectual state of a person from tweets. |
| Outcome: | The proposed model ranks 2nd, 4th and 6th in four of the four subtasks on SemEval-2018 task 1: Affect in Tweets. |
Joint Learning of Syntactic Features Helps Discourse Segmentation (2020.lrec-1)
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| Challenge: | Discourse segmentation is a task of fragmenting text into minimal disjoint chunks of text called Elementary Discourse Units (EDUs). |
| Approach: | They propose a framework for multi-lingual discourse segmentation with BERT . they cast the problem as a token classification problem and jointly learn syntactic features like part-of-speech tags and dependency relations. |
| Outcome: | Experiments in English, Dutch, German, Portuguese Brazilian and Basque show that the proposed model performs better across languages. |
Chinese Relation Classification using Long Short Term Memory Networks (L18-1)
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| Challenge: | Relation classification is the task to predict semantic relations between pairs of entities in a given text. |
| Approach: | They propose to extract relations between entities in Chinese text using a long-term memory network. |
| Outcome: | The proposed system achieves state-of-the-art F-measure on ACE 2005 corpus . it predicts relations between head entity e h and tail entity t from sentence . |
A Study on Entity Resolution for Email Conversations (2020.lrec-1)
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| Challenge: | This paper addresses the task of entity resolution in email conversations. |
| Approach: | They propose to create an annotated seed corpus of email threads labeled with entity coreference chains and evaluate their models for the task. |
| Outcome: | The proposed model performs well on the entity resolution task for email conversations. |