1,720,961 research outputs found
Statcan Dialogue Dataset
No full textWelcome to the data repository for requesting access to the Statcan Dialogue Dataset!
Before requesting access, you can visit our website or read our EACL 2023 paper
Requesting Access
In order to use our dataset, you must agree to the terms of use and restrictions before requesting access (see below). We will manually review each request and grant access or reach out to you for further information. To facilitate the process, make sure that:
Your Dataverse account is linked to your professional/research website, which we may review to ensure the dataset will be used for the intended purpose
Your request is made with an academic (e.g. .edu) or professional email (e.g. @servicenow.com). To do this, your have to set your primary email to your academic/professional email, or create a new Dataverse account.
If your academic institution does not end with .edu, or you are part of a professional group that does not have an email address, please contact us (see email in paper).
Abstract:
We introduce the StatCan Dialogue Dataset consisting of 19,379 conversation turns between agents working at Statistics Canada and online users looking for published data tables. The conversations stem from genuine intents, are held in English or French, and lead to agents retrieving one of over 5000 complex data tables. Based on this dataset, we propose two tasks: (1) automatic retrieval of relevant tables based on a on-going conversation, and (2) automatic generation of appropriate agent responses at each turn. We investigate the difficulty of each task by establishing strong baselines. Our experiments on a temporal data split reveal that all models struggle to generalize to future conversations, as we observe a significant drop in performance across both tasks when we move from the validation to the test set. In addition, we find that response generation models struggle to decide when to return a table. Considering that the tasks pose significant challenges to existing models, we encourage the community to develop models for our task, which can be directly used to help knowledge workers find relevant tables for live chat users. </blockquote
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
MeDAL
No full textMedical Dataset for Abbreviation Disambiguation for Natural Language Understanding (MeDAL) is a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. It was published at the ClinicalNLP workshop at EMNLP.
Paper
Code
Dataset (Kaggle)
Dataset (Zenodo)
Pre-trained ELECTRA (Hugging Face)
Downloading the data
We recommend downloading from Zenodo if you do not want to authenticate through Kaggle. The downside to Zenodo is that the data is uncompressed, so it will take more time to download. Links to the data can be found at the top of the readme. To download from Zenodo, simply do:
wget -nc -P data/ https://zenodo.org/record/4276178/files/full_data.csv
If you want to reproduce our pre-training results, you can download only the pre-training data below:
wget -nc -P data/ https://zenodo.org/record/4276178/files/train.csv
wget -nc -P data/ https://zenodo.org/record/4276178/files/valid.csv
wget -nc -P data/ https://zenodo.org/record/4276178/files/test.csv
Model Quickstart
Using Torch Hub
You can directly load LSTM and LSTM-SA with torch.hub:
import torch
lstm = torch.hub.load("BruceWen120/medal", "lstm")
lstm_sa = torch.hub.load("BruceWen120/medal", "lstm_sa")
If you want to use the Electra model, you need to first install transformers:
pip install transformers
Then, you can load it with torch.hub:
import torch
electra = torch.hub.load("BruceWen120/medal", "electra")
Using Huggingface transformers
If you are only interested in the pre-trained ELECTRA weights (without the disambiguation head), you can load it directly from the Hugging Face Repository:
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("xhlu/electra-medal")
tokenizer = AutoTokenizer.from_pretrained("xhlu/electra-medal")
Citation
Download the bibtex here, or copy the text below:
@inproceedings{wen-etal-2020-medal,
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
author = "Wen, Zhi and Lu, Xing Han and Reddy, Siva",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
pages = "130--135",
}
License, Terms and Conditions
The ELECTRA model is licensed under Apache 2.0. The license for the libraries used in this project (transformers, pytorch, etc.) can be found in their respective GitHub repository. Our model is released under a MIT license.
The original dataset was retrieved and modified from the NLM website. By using this dataset, you are bound by the terms and conditions specified by NLM:
INTRODUCTION
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