Search Results for author:
Found 15 papers, 9 papers with code
Comparative Opinion Summarization via Collaborative Decoding
Opinion summarization focuses on generating summaries that reflect popular subjective information expressed in multiple online reviews.
Annotating Columns with Pre-trained Language Models
Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information.
Ranked #1 on
Column Type Annotation
on VizNet-Sato-MultiColumn
Convex Aggregation for Opinion Summarization
We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpected $L_2$-norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration.
Ranked #1 on
Unsupervised Opinion Summarization
on Amazon
Extractive Opinion Summarization in Quantized Transformer Spaces
We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization.
Constructing Explainable Opinion Graphs from Review
The Web is a major resource of both factual and subjective information.
OpinionDigest: A Simple Framework for Opinion Summarization
The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Enhancing Review Comprehension with Domain-Specific Commonsense
In this paper, we introduce xSense, an effective system for review comprehension using domain-specific commonsense knowledge bases (xSense KBs).
Deep Entity Matching with Pre-Trained Language Models
Our experiments show that a straightforward application of language models such as BERT, DistilBERT, or RoBERTa pre-trained on large text corpora already significantly improves the matching quality and outperforms previous state-of-the-art (SOTA), by up to 29% of F1 score on benchmark datasets.
Ranked #2 on
Entity Resolution
on WDC Watches-xlarge
Understanding Human Judgments of Causality
We also analyzed the differences between the expert and non-expert machine algorithms based on their neural representations to evaluate the performances, providing insight into the human experts' and non-experts' cognitive abilities.
Sato: Contextual Semantic Type Detection in Tables
Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search.
Ranked #2 on
Column Type Annotation
on VizNet-Sato-MultiColumn
Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization
We present Emu, a system that semantically enhances multilingual sentence embeddings.
Happiness Entailment: Automating Suggestions for Well-Being
We prototype one necessary component of such a system, the Happiness Entailment Recognition (HER) module, which takes as input a short text describing an event, a candidate suggestion, and outputs a determination about whether the suggestion is more likely to be good for this user based on the event described.
Open Information Extraction from Question-Answer Pairs
We describe NeurON, a system for extracting tuples from question-answer pairs.
HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments
The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion.
A Lightweight Front-end Tool for Interactive Entity Population
Entity population, a task of collecting entities that belong to a particular category, has attracted attention from vertical domains.
