Natural language processing in ACM Computing Classification

NLP references

Models used in NLP

Text normalization

Part-of-speech Tagging

Sentiment Analysis

Topic Model

Parsing

High Dimensional Outputs

Historical Perspective: Natural Language Processing

Machine Reading and Comprehension

Minimum Edit Distance

Variation Factors of Input Texts

Period Disambiguation

Features Design for NLP Classification Problems

Vector Semantics and Embeddings

Words and Vectors

English Word Classes

Logical Representations of Sentence Meaning

First-Order Logic

Information Extraction

Word Senses

Semantic Roles: Labeling

Semantic Roles ( Thematic Roles )

Question Answering

Information Retrieval

Dialogue Systems

Properties of Human Conversation

Prompt Tuning

Types of NLP Model Paradigms

Types of Training Objectives of Pre-trained LM

Major Tuning Strategy Types

Articulatory Phonetics

Phonetics

Word embedding

A Survey of Data Augmentation Approaches for NLP

Data Augmentation in NLP

Spelling correction and the noisy channel

Constituency

Text Classification

Information Extraction (IE)

A Survey of Natural Language Based Financial Forecasting

More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction

A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization

From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

Machine Translation (MT)

Temporal Reasoning

Knowledge Graph

Dynamic Neural Network in Natural Language Processing

Label Preservation

Deep Learning Algorithms in Data Augmentation

Applications of Data Augmentation

Coreference Resolution

Explainable AI for Natural Language Processing

Corpora

Racism in NLP

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

Low-Resource Scenario in Natural Language Processing

A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios

Low-Resource NLP

Continual Learning

Continual Lifelong Learning in Natural Language Processing: A Survey

Object Naming in Language and Vision

A Survey on Hate Speech Detection using Natural Language Processing

Hate Speech Detection using Natural Language Processing

A Survey of Text Games for Reinforcement Learning informed by Natural Language

Natural Language Text Games for Reinforcement Learning

Data-Driven Sentence Simplification: Survey and Benchmark

Deep Learning for Text Style Transfer: A Survey

Text Style Transfer (TST)

Representing Numbers in NLP: a Survey and a Vision

Number representation in NLP

Semantic Textual Similarity (STS)

Paraphrase Identification (PI)

Machine Comprehension (MC)

Sentence Representation Model Categorizations

Automatic Detection of Machine Generated Text: A Critical Survey

Automatic Detection of Machine Generated Text

Fine-grained Financial Opinion Mining: A Survey and Research Agenda

Natural Language Processing in Finance

Phonology / Phonetics

Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering

Sentence Pair Modelling

A Survey of Active Learning for Text Classification using Deep Neural Networks

A Survey of Knowledge-Enhanced Text Generation

Knowledge-enhanced Text Generation

The Pollyanna Hypothesis

On Positivity Bias in Negative Reviews

Widely Used English Review Datasets

A Survey on Dialogue Summarization: Recent Advances and New Frontiers

Survey on Dialogue Summarization: Recent Advances and New Frontiers

Potential Biases of Natural Language Processing

The Pre-training and Fine-tuning Paradigm

Tokens and Words in NLP

Distinction and Interchangeability of 'Tokens' and 'Words' in NLP

Code-Switching in NLP and Linguistics

Automatic Speech Recognition

Text to Speech

Training Dataset

Word embedding

Analysis of an Early Neural Language Model's Innovation

What was the primary architectural innovation of the feed-forward neural language model introduced by Bengio et al. in 2003 that allowed it to overcome a major limitation of traditional statistical n-gram models?

A foundational 2003 study introduced a feed-forward neural network to predict the next word based on a fixed-size window of preceding words. Arrange the following steps in the correct order to describe how this model processes the input context to generate an output.