Handbook of Natural Language Processing

By Rick Dale, H. Moisl, H. Somers. Computational Linguistics, 2001

603 citations recorded by the source corpus.

Symbolic approaches to natural language processing tokenisation and sentence segmentation lexical analysis parsing techniques semantic analysis discourse structure and intention recognition natural language generation intelligent writing assistance database interfaces information extraction the generation of reports from databases the generation of multimedia presentations machine translation dialogue systems - from theory to practice in TRAINS-96 empirical approaches to natural language processing corpus creation for data-intensive linguistics part-of-speech tagging alignment contextual word similarity computing similarity collocations statistical parsing authorship identificaiton and computational stylometry lexical knowledge acquisition example-based machine translation word-sense disambiguation NLP based on artificial neural-networks - introduction.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/aab6f3b98cda74cfa252c710cf9b652afc024947

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In Artificial Intelligence

Citations in the corpus

On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation (2008, background): “…of many groups of researchers, with general NLP books dedicating separate chapters to WSD (Manning & Sch¨utze, 1999; Jurafsky & Martin, 2000; Dale, Moisl, & Somers, 2000), special issues on WSD in NLP journals (Ide & Veronis, 1998; Edmonds & Kilgarriﬀ, 2002), and books devoted speciﬁcally…”
Dissimilarity-based people re-identification and search for intelligent video surveillance (2013, background): “, a semantic engine able to interpret natural language can be used if the non structured query is a textual description of the person (a task often referred to as Natural Language Processing [69]).”
Opinion Mining and Information Fusion: A survey (2016, background): “This is simple to perform in space-delimited languages such as English, Spanish or French, but becomes considerably more difficult in languages where words are not delimited by spaces like in Japanese, Chinese and Thai [34].”
Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark (2024, background): “A. Definitions a) Text embedding : In the context of Natural Language Processing (NLP) or Natural Language Understanding (NLU), text refers to a collection of words, phrases, sentences, para-graphs or larger utterance that convey meaningful information [45].”