# Computational linguistics and Natural Language Processing

arXiv: https://arxiv.org/abs/2206.07026 · HTML: https://mlsanity.com/paper/2206.07026/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 75 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [Word Embeddings: A Survey](https://mlsanity.com/paper/1901.09069/) (2019)
- [Multilingual Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Models and Auxiliary Loss](https://mlsanity.com/paper/1604.05529/) (2016)
- [Multilingual Distributed Representations without Word Alignment](https://mlsanity.com/paper/1312.6173/) (2013)

## Abstract (arXiv)

This chapter provides an introduction to computational linguistics methods, with focus on their applications to the practice and study of translation. It covers computational models, methods and tools for collection, storage, indexing and analysis of linguistic data in the context of translation, and discusses the main methodological issues and challenges in this field. While an exhaustive review of existing computational linguistics methods and tools is beyond the scope of this chapter, we describe the most representative approaches, and illustrate them with descriptions of typical applications.