217 citations · 651 across the 24 of their papers we have counts for
10 papers · 1 filter
FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents
Bolun "Namir" Xia, Vipula D. Rawte, Mohammed J. Zaki +1
Unstructured data, especially text, continues to grow rapidly in various domains. In particular, in the financial sphere, there is a wealth of accumulated unstructured financial da…
Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings
Yuchen Liang, Mohammed J. Zaki
Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document. Most existing graph-based m…
Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph
Yu Chen, Ananya Subburathinam, Ching-Hua Chen +1
Food recommendation has become an important means to help guide users to adopt healthy dietary habits. Previous works on food recommendation either i) fail to consider users' expli…
Can a Fruit Fly Learn Word Embeddings?
Yuchen Liang, Chaitanya K. Ryali, Benjamin Hoover +4
The mushroom body of the fruit fly brain is one of the best studied systems in neuroscience. At its core it consists of a population of Kenyon cells, which receive inputs from mult…
Toward Subgraph-Guided Knowledge Graph Question Generation with Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Knowledge graph (KG) question generation (QG) aims to generate natural language questions from KGs and target answers. Previous works mostly focus on a simple setting which is to g…
A Framework for Generating Explanations from Temporal Personal Health Data
Jonathan J. Harris, Ching-Hua Chen, Mohammed J. Zaki
Whereas it has become easier for individuals to track their personal health data (e.g., heart rate, step count, food log), there is still a wide chasm between the collection of dat…
Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Natural question generation (QG) aims to generate questions from a passage and an answer. In this paper, we propose a novel reinforcement learning (RL) based graph-to-sequence (Gra…
Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Natural question generation (QG) aims to generate questions from a passage and an answer. Previous works on QG either (i) ignore the rich structure information hidden in text, (ii)…
GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension
Yu Chen, Lingfei Wu, Mohammed J. Zaki
Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However…
Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases
Yu Chen, Lingfei Wu, Mohammed J. Zaki
When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles. However, most existing embedding-based meth…