Results 21 to 30 of about 2,639,905 (339)

Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization [PDF]

arXiv.org, 2022
We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL) appears to be a natural conceptual framework. However, using
Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kianté Brantley, Jack Hessel, R. Sifa, C. Bauckhage, Hannaneh Hajishirzi, Yejin Choi   +7 more
semanticscholar   +1 more source

Readers and Reading in the First World War [PDF]

, 2015
This essay consists of three individually authored and interlinked sections. In ‘A Digital Humanities Approach’, Francesca Benatti looks at datasets and databases (including the UK Reading Experience Database) and shows how a systematic, macro-analytical
Edmund G. C. King, , Francesca Benatti, , Shafquat Towheed, , The Stanford Natural Language Processing Group, University of Sheffield   +4 more
core   +1 more source

Natural Language Processing in the Legal Domain [PDF]

Social Science Research Network, 2023
In this paper, we summarize the current state of the field of NLP&Law with a specific focus on recent technical and substantive developments. To support our analysis, we construct and analyze a nearly complete corpus of more than six hundred NLP&Law ...
D. Katz, D. Hartung, Lauritz Gerlach, Abhik Jana, M. Bommarito   +4 more
semanticscholar   +1 more source

Natural language processing [PDF]

, 2003
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information ...
Adams, Amsler, Bangalore, Barker, Benoît, Bian, Bondale, Carrick, Ceric, Chandrasekar, Chang, Charniak, Chen, Chowdhury, Chowdhury, Costantino, Cowie, Craven, Craven, Craven, Dogru, Evans, Feldman, Fernandez, Gaizauskas, Glasgow, Haas, Hayes, Hayes, Hedlund, Herath, Ide, Isahara, Jelinek, Jeong, Jurafsky, Kazakov, Kehler, Khoo, Kim, King, Lange, Lee, Lehmam, Lehtokangas, Lewis, Liddy, Liddy, Lovis, Ma, Magnini, Mani, Manning, Marquez, Martinez, Martinez, McMurchie, Meyer, Mihalcea, Mock, Moens, Morin, Narita, Nerbonne, Oard, Ogura, Oudet, Owei, Paris, Pasero, Pedersen, Perez-Carballo, Petreley, Pirkola, Poesio, Rosenfield, Roux, Say, Scarlett, Schenker, Silber, Smeaton, Smeaton, Smith, Sokol, Song, Sparck Jones, Staab, Stock, Tolle, Trybula, Tsuda, Vickery, Waldrop, Warner, Weigard, Wilks, Wong, Yang, Yang, Zadrozny, Zweigenbaum   +101 more
core   +1 more source

The Stanford CoreNLP Natural Language Processing Toolkit

Annual Meeting of the Association for Computational Linguistics, 2014
We describe the design and use of the Stanford CoreNLP toolkit, an extensible pipeline that provides core natural language analysis. This toolkit is quite widely used, both in the research NLP community and also among commercial and government users of ...
Christopher D. Manning, M. Surdeanu, John Bauer, J. Finkel, Steven Bethard, David McClosky   +5 more
semanticscholar   +1 more source

A Comprehensive Study of ChatGPT: Advancements, Limitations, and Ethical Considerations in Natural Language Processing and Cybersecurity

Inf., 2023
This paper presents an in-depth study of ChatGPT, a state-of-the-art language model that is revolutionizing generative text. We provide a comprehensive analysis of its architecture, training data, and evaluation metrics and explore its advancements and ...
Moatsum Alawida, S. Mejri, Abid Mehmood, B. Chikhaoui, Oludare Isaac Abiodun   +4 more
semanticscholar   +1 more source

ABDN at SemEval-2018 Task 10 : recognising discriminative attributes using context embeddings and WordNet [PDF]

, 2018
This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word’s similarity with each of the two semantically similar words, based on
Mao, Rui, Chen, G., Li, Ruizhe, Lin, Chenghua, Sub Natural Language Processing, Natural Language Processing   +5 more
core   +1 more source

Exploring Bi-Directional Context for Improved Chatbot Response Generation Using Deep Reinforcement Learning

Applied Sciences, 2023
The development of conversational agents that can generate relevant and meaningful replies is a challenging task in the field of natural language processing.
Quoc-Dai Luong Tran, Anh-Cuong Le
doaj   +1 more source

AllenNLP: A Deep Semantic Natural Language Processing Platform [PDF]

arXiv.org, 2018
Modern natural language processing (NLP) research requires writing code. Ideally this code would provide a precise definition of the approach, easy repeatability of results, and a basis for extending the research.
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew E. Peters, Michael Schmitz, Luke Zettlemoyer   +8 more
semanticscholar   +1 more source

Research on sentence alignment based on modeling word pairs [PDF]

Jisuanji gongcheng, 2019
Sentence alignment is a process mapping sentences in the source text to their counterparts in the target text.Within the framework of neural network,this paper proposes a sentence alignment method,on the basis that the aligned source sentence and target ...
DING Ying,LI Junhui,ZHOU Guodong
doaj   +1 more source