most citedA Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings

42 citations · 79 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2020

Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model

Guoxiu He, Zhe Gao, Zhuoren Jiang +4

The nonliteral interpretation of a text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language. In this study, inspir…

cs.CL201928 cited

Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis

Haiyun Peng, Lu Xu, Lidong Bing +3

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not l…

cs.CL20191 cited

Aligning Cross-Lingual Entities with Multi-Aspect Information

Hsiu-Wei Yang, Yanyan Zou, Peng Shi +3

Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a sourc…

cs.CL20192 cited

Text2Math: End-to-end Parsing Text into Math Expressions

Yanyan Zou, Wei Lu

We propose Text2Math, a model for semantically parsing text into math expressions. The model can be used to solve different math related problems including arithmetic word problems…

cs.CL20196 cited

Dependency-Guided LSTM-CRF for Named Entity Recognition

Zhanming Jie, Wei Lu

Dependency tree structures capture long-distance and syntactic relationships between words in a sentence. The syntactic relations (e.g., nominal subject, object) can potentially in…

cs.CL201942 cited

A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings

Wei Yang, Wei Lu, Vincent W. Zheng

Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The…