While designing machine learning based text analytics applications, often, NLP data scientists manually determine which NLP features to use based upon their knowledge and experience with related problems. This results in increased efforts during feature engineering process and renders automated reuse of features across semantically related applications inherently difficult. In this paper, we argue for standardization in feature specification by outlining structure of a language for specifying NLP features and present an approach for their reuse across applications to increase likelihood of identifying optimal features.
Comprehensive Implementation of TextCNN for Enhanced Collaboration between Natural Language Processing and System Recommendation
Northern Arizona University（北亚利桑那大学）；Stevens Institute of Technology（斯蒂文斯理工学院）；University of Sydney（悉尼大学）；Univeristy of California, Irvine（加利福尼亚大学尔湾分校）；Trine University（特莱恩大学）
University of York（约克大学）；University of Sheffield（谢菲尔德大学）
Natural language processing (NLP) methods for analyzing legal text offer legal scholars and practitioners a range of tools allowing to empirically analyze law on a large scale. However, researchers seem to struggle when it comes to identifying ethical limits to using NLP systems for acquiring genuine insights both about the law and the systems' predictive capacity. In this paper we set out a number of ways in which to think systematically about such issues. We place emphasis on three crucial normative parameters which have, to the best of our knowledge, been underestimated by current debates: …
Information School, the University of Sheffield（谢菲尔德大学信息学院）；Vamstar Ltd.（Vamstar有限公司）
While text mining and NLP research has been established for decades, there remain gaps in the literature that reports the use of these techniques in building real-world applications. For example, they typically look at single and sometimes simplified tasks, and do not discuss in-depth data heterogeneity and inconsistency that is common in real-world problems or their implication on the development of their methods. Also, few prior work has focused on the healthcare domain. In this work, we describe an industry project that developed text mining and NLP solutions to mine millions of heterogeneo…
State-of-the-art generalisation research in NLP: A taxonomy and review
FAIR；University of Amsterdam（阿姆斯特丹大学）；ETH Zürich（苏黎世联邦理工学院）；University of Edinburgh（爱丁堡大学）；Reka AI；Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；University of Cambridge（剑桥大学）；Amazon Alexa AI（亚马逊Alexa AI）；École Normale Supérieure-PSL（巴黎高等师范学院-PSL）；The World Bank（世界银行）；Harvard University（哈佛大学）；University of Aberdeen（阿伯丁大学）；IT University of Copenhagen（哥本哈根信息技术大学）；Pioneer Centre for Artificial Intelligence（先锋人工智能中心）；Textshuttle；National University of Mongolia（蒙古国立大学）；Hong Kong University of Science and Technology（香港科技大学）；MIT（麻省理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
GenRecEdit: Adapting Model Editing for Generative Recommendation with Cold-Start Items
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学全球化人工智能学院）；School of Information Technology and Management, University of International Business and Economics（国际经济贸易大学信息科学技术与管理学院）
Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation
Centre for Frontier AI Research（前沿人工智能研究中心）；Institute of High Performance Computing（高性能计算研究所）；A*STAR（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）；Hong Kong Polytechnic University（香港理工大学）
LLMRec: Benchmarking Large Language Models on Recommendation Task
Alibaba Group（阿里巴巴集团）；Ant Group（蚂蚁集团）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Peking University（北京大学）；The Hong Kong University of Science and Technology（香港科技大学）；University of Technology Sydney（悉尼科技大学）；Yale University（耶鲁大学）；University of Illinois Chicago（芝加哥伊利诺伊大学）
A Survey on Large Language Models for Recommendation
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（中国科学技术大学认知智能国家重点实验室）；Career Science Lab, BOSS Zhipin（BOSS直聘职业科学实验室）
IT University of Copenhagen（哥本哈根信息技术大学）；Uppsala University（乌普萨拉大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Google AI Language（谷歌人工智能语言部门）；Canva；Microsoft Research（微软研究院）
At the Workshop on Gender Bias in NLP (GeBNLP), we'd like to encourage authors to give explicit consideration to the wider aspects of bias and its social implications. For the 2020 edition of the workshop, we therefore requested that all authors include an explicit bias statement in their work to clarify how their work relates to the social context in which NLP systems are used. The programme committee of the workshops included a number of reviewers with a background in the humanities and social sciences, in addition to NLP experts doing the bulk of the reviewing. Each paper was assigned one o…
Columbia University（哥伦比亚大学）；IBM Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）
We propose a recommendation system that suggests treatment strategies to a therapist during the psychotherapy session in real-time. Our system uses a turn-level rating mechanism that predicts the therapeutic outcome by computing a similarity score between the deep embedding of a scoring inventory, and the current sentence that the patient is speaking. The system automatically transcribes a continuous audio stream and separates it into turns of the patient and of the therapist and perform real-time inference of their therapeutic working alliance. The dialogue pairs along with their computed wor…
Department of Computer Science, Yale University（耶鲁大学计算机科学系）
The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources. In order to learn this dynamic field or stay up-to-date on the latest research, students as well as educators and researchers must constantly sift through multiple sources to find valuable, relevant information. To address this situation, we introduce TutorialBank, a new, publicly available dataset which aims to facilitate NLP education and research. We have manually collected and categorized over 6,300 resources on NLP…
College of Information and Computer Sciences（信息与计算机科学学院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Microsoft Research（微软研究院）；Cornell University（康奈尔大学）；University of Maryland（马里兰大学）
We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These …
Text generation has long been a popular research topic in NLP. However, the task of generating recruitment emails from recruiters to candidates in the job recommendation scenario has received little attention by the research community. This work aims at defining the topic of automatic email generation for job recommendation, identifying the challenges, and providing a baseline template-based solution for Danish jobs. Evaluation by human experts shows that our method is effective. We wrap up by discussing the future research directions for better solving this task.
University of Copenhagen（哥本哈根大学）；RIKEN Center for Computational Science（理化学研究所计算科学中心）
Both scientific progress and individual researcher careers depend on the quality of peer review, which in turn depends on paper-reviewer matching. Surprisingly, this problem has been mostly approached as an automated recommendation problem rather than as a matter where different stakeholders (area chairs, reviewers, authors) have accumulated experience worth taking into account. We present the results of the first survey of the NLP community, identifying common issues and perspectives on what factors should be considered by paper-reviewer matching systems. This study contributes actionable rec…
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））
Clinical text processing has gained more and more attention in recent years. The access to sensitive patient data, on the other hand, is still a big challenge, as text cannot be shared without legal hurdles and without removing personal information. There are many techniques to modify or remove patient related information, each with different strengths. This paper investigates the influence of different anonymization techniques on the performance of ML models using multiple datasets corresponding to five different NLP tasks. Several learnings and recommendations are presented. This work confir…
WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain
University of Copenhagen（哥本哈根大学）；Edinburgh Napier University（爱丁堡纳皮尔大学）；Leiden University（莱顿大学）；University of Amsterdam（阿姆斯特丹大学）；Ghent University - imec（根特大学 - imec）；AIDA-IDLab, Ghent University（AIDA-IDLab，根特大学）；IT University of Copenhagen（哥本哈根IT大学）
Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMs
Erasmus Mundus Master in Language and Communication Technologies（埃拉斯谟 Mundus 语言与沟通技术硕士项目）；HiTZ Center - Ixa, University of the Basque Country (UPV/EHU)（巴斯克国家大学（UPV/EHU）HiTZ中心 - Ixa）；Cohere（Cohere公司）；Saarland University（萨尔兰州大学）
Natural Language Processing (NLP) is increasingly used as a key ingredient in critical decision-making systems such as resume parsers used in sorting a list of job candidates. NLP systems often ingest large corpora of human text, attempting to learn from past human behavior and decisions in order to produce systems that will make recommendations about our future world. Over 7000 human languages are being spoken today and the typical NLP pipeline underrepresents speakers of most of them while amplifying the voices of speakers of other languages. In this paper, a team including speakers of 8 lan…
Natural language processing (NLP) systems have become a central technology in communication, education, medicine, artificial intelligence, and many other domains of research and development. While the performance of NLP methods has grown enormously over the last decade, this progress has been restricted to a minuscule subset of the world's 6,500 languages. We introduce a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. Our analyses involve the field at large, but also more in-depth studies on both user-f…
University of British Columbia（不列颠哥伦比亚大学）；Huawei Cananda Technologies Co. Ltd.（华为加拿大技术有限公司）
Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual analytic framework for assisting researchers in such process, by providing them with valuable insights about the model's intrinsic properties and behaviours. Our framework offers an intuitive overview that allows the user to explore different facets of the model (e.g., hidden states, attention) through interactive visualization, and allows a suite of built-in algorithms that compute the importance of model component…
Recent advances in Big Data has prompted health care practitioners to utilize the data available on social media to discern sentiment and emotions expression. Health Informatics and Clinical Analytics depend heavily on information gathered from diverse sources. Traditionally, a healthcare practitioner will ask a patient to fill out a questionnaire that will form the basis of diagnosing the medical condition. However, medical practitioners have access to many sources of data including the patients writings on various media. Natural Language Processing (NLP) allows researchers to gather such dat…
UNC Charlotte（北卡罗来纳大学夏洛特分校）；U. of Central Florida（中佛罗里达大学）
We introduce a formal distinction between contradictions and disagreements in natural language texts, motivated by the need to formally reason about contradictory medical guidelines. This is a novel and potentially very useful distinction, and has not been discussed so far in NLP and logic. We also describe a NLP system capable of automated finding contradictory medical guidelines; the system uses a combination of text analysis and information retrieval modules. We also report positive evaluation results on a small corpus of contradictory medical recommendations.
King Fahd University of Petroleum and Minerals（法赫德石油和矿产大学）
The NLP pipeline has evolved dramatically in the last few years. The first step in the pipeline is to find suitable annotated datasets to evaluate the tasks we are trying to solve. Unfortunately, most of the published datasets lack metadata annotations that describe their attributes. Not to mention, the absence of a public catalogue that indexes all the publicly available datasets related to specific regions or languages. When we consider low-resource dialectical languages, for example, this issue becomes more prominent. In this paper we create \textit{Masader}, the largest public catalogue fo…
University of Washington（华盛顿大学）；Snowflake Computing（斯诺flake计算公司）
We consider methods for learning vector representations of SQL queries to support generalized workload analytics tasks, including workload summarization for index selection and predicting queries that will trigger memory errors. We consider vector representations of both raw SQL text and optimized query plans, and evaluate these methods on synthetic and real SQL workloads. We find that general algorithms based on vector representations can outperform existing approaches that rely on specialized features. For index recommendation, we cluster the vector representations to compress large workload…
Language models that utilize extensive self-supervised pre-training from unlabeled text, have recently shown to significantly advance the state-of-the-art performance in a variety of language understanding tasks. However, it is yet unclear if and how these recent models can be harnessed for conducting text-based recommendations. In this work, we introduce RecoBERT, a BERT-based approach for learning catalog-specialized language models for text-based item recommendations. We suggest novel training and inference procedures for scoring similarities between pairs of items, that don't require item …
The use of large pretrained neural networks to create contextualized word embeddings has drastically improved performance on several natural language processing (NLP) tasks. These computationally expensive models have begun to be applied to domain-specific NLP tasks such as re-hospitalization prediction from clinical notes. This paper demonstrates that using large pretrained models produces excellent results on common learning analytics tasks. Pre-training deep language models using student forum data from a wide array of online courses improves performance beyond the state of the art on three…
The rise of big data analytics on top of NLP increases the computational burden for text processing at scale. The problems faced in NLP are very high dimensional text, so it takes a high computation resource. The MapReduce allows parallelization of large computations and can improve the efficiency of text processing. This research aims to study the effect of big data processing on NLP tasks based on a deep learning approach. We classify a big text of news topics with fine-tuning BERT used pre-trained models. Five pre-trained models with a different number of parameters were used in this study.…
