MBT: A Memory-Based Part of Speech Tagger-Generator
We introduce a memory-based approach to part of speech tagging. Memory-based learning is a form of supervised learning based on similarity-based reasoning. The part of speech tag of a word in a particular context is extrapolated from the most similar cases held in memory. Supervised learning approaches are useful when a tagged corpus is available as an example of the desired output of the tagger. Based on such a corpus, the tagger-generator automatically builds a tagger which is able to tag new text the same way, diminishing development time for the construction of a tagger considerably. Memory-based tagging shares this advantage with other statistical or machine learning approaches. Additional advantages specific to a memory-based approach include (i) the relatively small tagged corpus size sufficient for training, (ii) incremental learning, (iii) explanation capabilities, (iv) flexible integration of information in case representations, (v) its non-parametric nature, (vi) reasonably good results on unknown words without morphological analysis, and (vii) fast learning and tagging. In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using {\em IGTree}, a tree-based formalism for indexing and searching huge case bases.} The use of IGTree has as additional advantage that optimal context size for disambiguation is dynamically computed.
Code (1)
Tasks
Incremental LearningMorphological AnalysisPart-Of-Speech TaggingTAGSimilar Papers 제목 키워드 기반
Detecting Syntactic Change Using a Neural Part-of-Speech Tagger
We train a diachronic long short-term memory (LSTM) part-of-speech tagger on a large corpus of American English from the 19th, 20th, and 21st centuries. We analyze the tagger's ability to implicitly learn temporal struct…
An open source part-of-speech tagger for Norwegian: Building on existing language resources
This paper presents an open source part-of-speech tagger for the Norwegian language. It describes how an existing language processing library (FreeLing) was used to build a new part-of-speech tagger for this language. Th…
Dependency ParsingMachine TranslationMorphological AnalysisMorphological Tagging+1Tagging a Norwegian Dialect Corpus
This paper describes an evaluation of five data-driven part-of-speech (PoS) taggers for spoken Norwegian. The taggers all rely on different machine learning mechanisms: decision trees, hidden Markov models (HMMs), condit…
POSDevelopment of Marathi Part of Speech Tagger Using Statistical Approach
Part-of-speech (POS) tagging is a process of assigning the words in a text corresponding to a particular part of speech. A fundamental version of POS tagging is the identification of words as nouns, verbs, adjectives etc…
Information RetrievalPart-Of-Speech TaggingPOSPOS Tagging+7An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags
This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natu…
AttributePart-Of-Speech Tagging