Search Results for author:
Found 26 papers, 12 papers with code
Reimagining Retrieval Augmented Language Models for Answering Queries
We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison.
TimelineQA: A Benchmark for Question Answering over Timelines
We describe a set of experiments on TimelineQA with several state-of-the-art QA models.
Unstructured and structured data: Can we have the best of both worlds with large language models?
This paper presents an opinion on the potential of using large language models to query on both unstructured and structured data.
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
Deep or Simple Models for Semantic Tagging? It Depends on your Data [Experiments]
We embark on a systematic study to investigate the following question: Are deep models the best performing model for all semantic tagging tasks?
Constructing Explainable Opinion Graphs from Review
The Web is a major resource of both factual and subjective information.
Adaptive Rule Discovery for Labeling Text Data
At the same time, creating a labeled subset of the data can be costly and even infeasible in imbalanced settings.
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
SubjQA: A Dataset for Subjectivity and Review Comprehension
We find that subjectivity is also an important feature in the case of QA, albeit with more intricate interactions between subjectivity and QA performance.
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
Towards Productionizing Subjective Search Systems
Also, we found that the existing search algorithms do not meet the search quality standard required by production systems.
Sampo: Unsupervised Knowledge Base Construction for Opinions and Implications
Since it can be expensive to obtain training data to learn to extract implications for each new domain of reviews, we propose an unsupervised KBC system, Sampo, Specifically, Sampo is tailored to build KBs for domains where many reviews on the same domain are available.
Snippext: Semi-supervised Opinion Mining with Augmented Data
A novelty of Snippext is its clever use of a two-prong approach to achieve state-of-the-art (SOTA) performance with little labeled training data through: (1) data augmentation to automatically generate more labeled training data from existing ones, and (2) a semi-supervised learning technique to leverage the massive amount of unlabeled data in addition to the (limited amount of) labeled data.
Teddy: A System for Interactive Review Analysis
They contain a wealth of information about the opinions and experiences of users, which can help better understand consumer decisions and improve user experience with products and services.
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
Essentia: Mining Domain-Specific Paraphrases with Word-Alignment Graphs
Paraphrases are important linguistic resources for a wide variety of NLP applications.
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.
Voyageur: An Experiential Travel Search Engine
We describe Voyageur, which is an application of experiential search to the domain of travel.
Open Information Extraction from Question-Answer Pairs
We describe NeurON, a system for extracting tuples from question-answer pairs.
Subjective Databases
In order to support experiential queries, a database system needs to model subjective data and also be able to process queries where the user can express varied subjective experiences in words chosen by the user, in addition to specifying predicates involving objective attributes.
Databases
FrameIt: Ontology Discovery for Noisy User-Generated Text
A common need of NLP applications is to extract structured data from text corpora in order to perform analytics or trigger an appropriate action.
Scalable Semantic Querying of Text
KOKO is novel in that its extraction language simultaneously supports conditions on the surface of the text and on the structure of the dependency parse tree of sentences, thereby allowing for more refined extractions.
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.
