# Natural Language Processing Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/natural-language-processing-techniques/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on statistical machine translation, neural machine translation, dependency parsing, word sense disambiguation, and part-of-speech tagging. It also covers topics such as corpus linguistics, syntax-based translation models, multilingual neural machine translation, and language modeling. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10181 |
| Works | 159 |

## Topic papers all

Showing 15 of 159.

- [The lexical nature of syntactic ambiguity resolution.](https://scholariq.org/papers/the-lexical-nature-of-syntactic-ambiguity-resolution/)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://scholariq.org/papers/tinybert-distilling-bert-for-natural-language-understanding/)
- [An analysis of active learning strategies for sequence labeling tasks](https://scholariq.org/papers/an-analysis-of-active-learning-strategies-for-sequence-labeling-tasks/)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://scholariq.org/papers/ctrl-a-conditional-transformer-language-model-for-controllable-generation/)
- [Applying Conditional Random Fields to Japanese Morphological Analysis](https://scholariq.org/papers/applying-conditional-random-fields-to-japanese-morphological-analysis/)
- [Learning to extract symbolic knowledge from the World Wide Web](https://scholariq.org/papers/learning-to-extract-symbolic-knowledge-from-the-world-wide-web/)
- [Statistical Dependency Analysis with Support Vector Machines](https://scholariq.org/papers/statistical-dependency-analysis-with-support-vector-machines/)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://scholariq.org/papers/luke-deep-contextualized-entity-representations-with-entity-aware-self-attention/)
- [Chunking with support vector machines](https://scholariq.org/papers/chunking-with-support-vector-machines/)
- [The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies](https://scholariq.org/papers/the-conll-2008-shared-task-on-joint-parsing-of-syntactic-and-semantic/)
- [Japanese dependency analysis using cascaded chunking](https://scholariq.org/papers/japanese-dependency-analysis-using-cascaded-chunking/)
- [VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research](https://scholariq.org/papers/vatex-a-large-scale-high-quality-multilingual-dataset-for-video-and-language/)
- [Incorporating domain knowledge into topic modeling via Dirichlet Forest priors](https://scholariq.org/papers/incorporating-domain-knowledge-into-topic-modeling-via-dirichlet-forest-priors/)
- [Long Text Generation via Adversarial Training with Leaked Information](https://scholariq.org/papers/long-text-generation-via-adversarial-training-with-leaked-information/)
- [Toward Distributed Use of Large-Scale Ontologies t](https://scholariq.org/papers/toward-distributed-use-of-large-scale-ontologies-t/)

## Topic primary papers

Showing 15 of 50.

- [Applying Conditional Random Fields to Japanese Morphological Analysis](https://scholariq.org/papers/applying-conditional-random-fields-to-japanese-morphological-analysis/)
- [Statistical Dependency Analysis with Support Vector Machines](https://scholariq.org/papers/statistical-dependency-analysis-with-support-vector-machines/)
- [Chunking with support vector machines](https://scholariq.org/papers/chunking-with-support-vector-machines/)
- [The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies](https://scholariq.org/papers/the-conll-2008-shared-task-on-joint-parsing-of-syntactic-and-semantic/)
- [Japanese dependency analysis using cascaded chunking](https://scholariq.org/papers/japanese-dependency-analysis-using-cascaded-chunking/)
- [Wide-Coverage Efficient Statistical Parsing with CCG and Log-Linear Models](https://scholariq.org/papers/wide-coverage-efficient-statistical-parsing-with-ccg-and-log-linear-models/)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://scholariq.org/papers/no-language-left-behind-scaling-human-centered-machine-translation/)
- [The CoNLL-2009 shared task](https://scholariq.org/papers/the-conll-2009-shared-task/)
- [Offline bilingual word vectors, orthogonal transformations and the\n inverted softmax](https://scholariq.org/papers/offline-bilingual-word-vectors-orthogonal-transformations-and-the-n-inverted/)
- [Use of support vector learning for chunk identification](https://scholariq.org/papers/use-of-support-vector-learning-for-chunk-identification/)
- [Extended constituent-to-dependency conversion for English](https://scholariq.org/papers/extended-constituent-to-dependency-conversion-for-english/)
- [Offline bilingual word vectors, orthogonal transformations and the inverted softmax](https://scholariq.org/papers/offline-bilingual-word-vectors-orthogonal-transformations-and-the-inverted/)
- [Linguistically motivated large-scale NLP with C&C and boxer](https://scholariq.org/papers/linguistically-motivated-large-scale-nlp-with-c-and-amp-c-and-boxer/)
- [Using a semantic concordance for sense identification](https://scholariq.org/papers/using-a-semantic-concordance-for-sense-identification/)
- [Vector Space Models of Lexical Meaning](https://scholariq.org/papers/vector-space-models-of-lexical-meaning/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.