Search Results for author:
Found 12 papers, 3 papers with code
Identifying Principals and Accessories in a Complex Case based on the Comprehension of Fact Description
In this paper, we study the problem of identifying the principals and accessories from the fact description with multiple defendants in a criminal case.
A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature
We introduce a new task of modeling the role and function for on-line resource citations in scientific literature.
Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction
Furthermore, to deal with the problem of class imbalance in distant supervision relation extraction, we further adopt cost-sensitive learning to rescale the costs from the positive and negative labels.
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
Event extraction is of practical utility in natural language processing.
Interpretable Rationale Augmented Charge Prediction System
This paper proposes a neural based system to solve the essential interpretability problem existing in text classification, especially in charge prediction task.
Real-time Scholarly Retweeting Prediction System
At last, we combine scholarly features with the Tweet Scholar Blocks to predict whether a scholarly tweet will be retweeted.
CRST: a Claim Retrieval System in Twitter
For controversial topics, collecting argumentation-containing tweets which tend to be more convincing will help researchers analyze public opinions.
IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification
We experimented on this task with two methods: CNN method and traditional pipeline method.
Joker at SemEval-2018 Task 12: The Argument Reasoning Comprehension with Neural Attention
This paper describes a classification system that participated in the SemEval-2018 Task 12: The Argument Reasoning Comprehension Task.
Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions
In this paper, we propose to study the problem of COURT VIEW GENeration from the fact description in a criminal case.
Jointly Extracting Relations with Class Ties via Effective Deep Ranking
Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction.
