NING MA et al: FUSION OF WORD CLUSTERING FEATURES FOR TIBETAN PART OF SPEECH TAGGING
نویسندگان
چکیده
Tibetan Part of Speech (POS) tagging, the foundation of Tibetan natural language processing, judges word classification according to contextual information of words. Based on the framework of the maximum entropy model, the paper studied the fusion of morphological features for Tibetan part of speech with maximum entropy model with the integration of word clustering features. Experimental results show that Tibetan POS based on maximum entropy achieves much better results and word cluster features can increase the performance of Tibetan POS significantly. Additionally, the accuracy rate of Tibetan POS based on maximum entropy is 0.81% higher than that of baseline system.
منابع مشابه
An improved joint model: POS tagging and dependency parsing
Dependency parsing is a way of syntactic parsing and a natural language that automatically analyzes the dependency structure of sentences, and the input for each sentence creates a dependency graph. Part-Of-Speech (POS) tagging is a prerequisite for dependency parsing. Generally, dependency parsers do the POS tagging task along with dependency parsing in a pipeline mode. Unfortunately, in pipel...
متن کاملSyntactic Category Learning as Iterative Prototype-Driven Clustering
We lay out a model for minimally supervised syntactic category acquisition which combines psychologically plausible concepts from standard NLP part-of-speech tagging applications with simple cognitively motivated distributional statistics. The model assumes a small set of seed words (Haghighi and Klein, 2006), an approach with motivation in (Pinker, 1984)’s semantic bootstrapping hypothesis, an...
متن کاملFuzzy Clustering Approach Using Data Fusion Theory and its Application To Automatic Isolated Word Recognition
In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the...
متن کاملبرچسبگذاری ادات سخن زبان فارسی با استفاده از مدل شبکۀ فازی
Part of speech tagging (POS tagging) is an ongoing research in natural language processing (NLP) applications. The process of classifying words into their parts of speech and labeling them accordingly is known as part-of-speech tagging, POS-tagging, or simply tagging. Parts of speech are also known as word classes or lexical categories. The purpose of POS tagging is determining the grammatical ...
متن کاملUnsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models
We tackle unsupervised part-of-speech (POS) tagging by learning hidden Markov models (HMMs) that are particularly well-suited for the problem. These HMMs, which we call anchor HMMs, assume that each tag is associated with at least one word that can have no other tag, which is a relatively benign condition for POS tagging (e.g., “the” is a word that appears only under the determiner tag). We exp...
متن کامل