Machine Learning

Anaphora Resolution ◽

Syntactic Parsing ◽

Word Sense ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

Sense Disambiguation ◽

Training Examples ◽

This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described by a set of features. These techniques can be applied to learn knowledge required for a variety of problems in computational linguistics ranging from part-of-speech tagging and syntactic parsing to word-sense disambiguation and anaphora resolution. Finally, this article reviews the applications to a variety of these problems, such as morphology, part-of-speech tagging, word-sense disambiguation, syntactic parsing, semantic parsing, information extraction, and anaphora resolution.

Machine Learning

The Oxford Handbook of Computational Linguistics 2nd edition ◽

10.1093/oxfordhb/9780199573691.013.016 ◽

2018 ◽

Author(s):

Raymond J. Mooney

Keyword(s):

Machine Learning ◽

Computational Linguistics ◽

Anaphora Resolution ◽

Word Sense ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

New Concepts ◽

Training Examples ◽

This chapter introduces symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. It also briefly reviews unsupervised learning, in which new concepts are formed from unannotated examples by clustering them into coherent groups. These techniques can be applied to learn knowledge required for a variety of problems in computational linguistics ranging from part-of-speech tagging and syntactic parsing to word sense disambiguation and anaphora resolution. Applications to a variety of these problems are reviewed.

Word-Sense Disambiguation

10.1093/oxfordhb/9780199276349.013.0013 ◽

2012 ◽

Cited By ~ 1

Author(s):

Mark Stevenson ◽

Yorick Wilks

Keyword(s):

Language Processing ◽

Syntactic Analysis ◽

Word Sense ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

Evaluation Strategies ◽

Sense Disambiguation ◽

Translation Systems ◽

Word-sense disambiguation (WSD) is the process of identifying the meanings of words in context. This article begins with discussing the origins of the problem in the earliest machine translation systems. Early attempts to solve the WSD problem suffered from a lack of coverage. The main approaches to tackle the problem were dictionary-based, connectionist, and statistical strategies. This article concludes with a review of evaluation strategies for WSD and possible applications of the technology. WSD is an ‘intermediate’ task in language processing: like part-of-speech tagging or syntactic analysis, it is unlikely that anyone other than linguists would be interested in its results for their own sake. ‘Final’ tasks produce results of use to those without a specific interest in language and often make use of ‘intermediate’ tasks. WSD is a long-standing and important problem in the field of language processing.

Evaluating sense disambiguation across diverse parameter spaces

Natural Language Engineering ◽

10.1017/s135132490200298x ◽

2002 ◽

Vol 8 (4) ◽

pp. 293-310 ◽

Cited By ~ 45

Author(s):

DAVID YAROWSKY ◽

RADU FLORIAN

Keyword(s):

Model Performance ◽

Training Data ◽

Target Language ◽

Word Sense ◽

Parameter Spaces ◽

Diverse Range ◽

Part Of Speech ◽

Sense Disambiguation ◽

Training Examples

This paper presents a comprehensive empirical exploration and evaluation of a diverse range of data characteristics which influence word sense disambiguation performance. It focuses on a set of six core supervised algorithms, including three variants of Bayesian classifiers, a cosine model, non-hierarchical decision lists, and an extension of the transformation-based learning model. Performance is investigated in detail with respect to the following parameters: (a) target language (English, Spanish, Swedish and Basque); (b) part of speech; (c) sense granularity; (d) inclusion and exclusion of major feature classes; (e) variable context width (further broken down by part-of-speech of keyword); (f) number of training examples; (g) baseline probability of the most likely sense; (h) sense distributional entropy; (i) number of senses per keyword; (j) divergence between training and test data; (k) degree of (artificially introduced) noise in the training data; (l) the effectiveness of an algorithm's confidence rankings; and (m) a full keyword breakdown of the performance of each algorithm. The paper concludes with a brief analysis of similarities, differences, strengths and weaknesses of the algorithms and a hierarchical clustering of these algorithms based on agreement of sense classification behavior. Collectively, the paper constitutes the most comprehensive survey of evaluation measures and tests yet applied to sense disambiguation algorithms. And it does so over a diverse range of supervised algorithms, languages and parameter spaces in single unified experimental framework.

Klasifikasi Kelas Kata (Part-Of-Speech Tagging) untuk Bahasa Madura Menggunakan Algoritme Viterbi

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021854483 ◽

2021 ◽

Vol 8 (5) ◽

pp. 1039

Author(s):

Ilham Firmansyah ◽

Putra Pandu Adikara ◽

Sigit Adinugroho

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Viterbi Algorithm ◽

Confusion Matrix ◽

Word Sense ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

Bahasa manusia adalah bahasa yang digunakan oleh manusia dalam bentuk tulisan maupun suara. Banyak teknologi/aplikasi yang mengolah bahasa manusia, bidang tersebut bernama Natural Language Processing yang merupakan ilmu yang mempelajari untuk mengolah dan mengekstraksi bahasa manusia pada perkembangan teknologi. Salah satu proses pada Natural Language Processing adalah Part-Of-Speech Tagging. Part-Of-Speech Tagging adalah klasifikasi kelas kata pada sebuah kalimat secara otomatis oleh teknologi, proses ini salah satunya berfungsi untuk mengetahui kata-kata yang memiliki lebih dari satu makna/arti (ambiguitas). Part-Of-Speech Tagging merupakan dasar dari Natural Language Processing lainnya, seperti penerjemahan mesin (machine translation), penghilangan ambiguitas makna kata (word sense disambiguation), dan analisis sentimen. Part-Of-Speech Tagging dilakukan pada bahasa manusia, salah satunya adalah bahasa Madura. Bahasa Madura adalah bahasa daerah yang digunakan oleh suku Madura dan memiliki morfologi yang mirip dengan bahasa Indonesia. Penelitian pada Part-Of-Speech Tagging pada bahasa Madura ini menggunakan algoritme Viterbi, terdapat 3 proses untuk implementasi algoritme Viterbi pada pada Part-Of-Speech Tagging bahasa Madura, yaitu pre-processing pada data training dan testing, perhitungan data latih dengan Hidden Markov Model dan klasifikasi kelas kata menggunakan algoritme Viterbi. Kelas kata (tagset) yang digunakan untuk klasifikasi kata pada bahasa Madura sebanyak 19 kelas, kelas kata tersebut dirancang oleh pakar. Pengujian sistem pada penelitian ini menggunakan perhitungan Multiclass Confusion Matrix. Hasil pengujian sistem mendapatkan nilai micro average accuracy sebesar 0,96 dan nilai micro average precision dan recall yang sama sebesar 0,68. Precision dan recall masih dapat ditingkatkan dengan menambahkan data yang lebih banyak lagi untuk pelatihan. AbstractNatural language is a form of language used by human, either in writing or speaking form. There is a specific field in computer science that processes natural language, which is called Natural Language Processing. It is a study of how to process and extract natural language on technology development. Part-Of-Speech Tagging is a method to assign a predefined set of tags (word classes) into a word or a phrase. This process is useful to understand the true meaning of a word with ambiguous meaning, which may have different meanings depending on the context. Part-Of-Speech Tagging is the basis of the other Natural Language Processing methods, such as machine translation, word sense disambiguation, and sentiment analysis. Part-Of-Speech Tagging used in natural languages, such as Madurese language. Madurese language is a local language used by Madurese and has a similar morphology as Indonesian language. Part-Of-Speech Tagging research on Madurese language using Viterbi algorithm, consists of 3 processes, which are training and testing corpus pre-processing, training the corpus by Hidden Markov Model, and tag classification using Viterbi algorithm. The number of tags used for words classification (tagsets) on Madurese language are 19 class, those tags were designed by an expert. Performance assessment was conducted using Multiclass Confusion Matrix calculation. The system achieved a micro average accuracy score of 0,96, and micro average precision score is equal to recall of 0,68. Precision and recall can still be improved by adding more data for training.

Improving term extraction by combining different techniques

Terminology ◽

10.1075/term.7.1.04viv ◽

2001 ◽

Vol 7 (1) ◽

pp. 31-48 ◽

Cited By ~ 9

Author(s):

Jorge Vivaldi ◽

Horacio Rodríguez

Keyword(s):

Word Sense ◽

Context Sensitive ◽

Part Of Speech Tagging ◽

Hybrid Approaches ◽

Part Of Speech ◽

Term Extraction ◽

Automatic Term Extraction ◽

The One ◽

Two different reasons suggest that combining the performance of several term extractors could lead to an improvement in overall system accuracy. On the one hand, there is no clear agreement on whether to follow statistical, linguistic or hybrid approaches for (semi-) automatic term extraction. On the other hand, combining different knowledge sources (e.g. classifiers) has proved successful in improving the performance of individual sources on several NLP tasks (some of them closely related to or involved in term extraction), such as context-sensitive spelling correction, part-of-speech tagging, word sense disambiguation, parsing, text classification and filtering, etc. In this paper, we present a proposal for combining a number of different term extraction techniques in order to improve the accuracy of the resulting system. The approach has been applied to the domain of medicine for the Spanish language. A number of tests have been carried out with encouraging results.

NP Animacy Identification for Anaphora Resolution

Journal of Artificial Intelligence Research ◽

10.1613/jair.2179 ◽

2007 ◽

Vol 29 ◽

pp. 79-103 ◽

Cited By ~ 11

Author(s):

C. Orasan ◽

R. J. Evans

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Anaphora Resolution ◽

Machine Learning Algorithm ◽

Machine Learning Method ◽

Word Sense ◽

Rule Based ◽

Sense Disambiguation ◽

Integral Role

In anaphora resolution for English, animacy identification can play an integral role in the application of agreement restrictions between pronouns and candidates, and as a result, can improve the accuracy of anaphora resolution systems. In this paper, two methods for animacy identification are proposed and evaluated using intrinsic and extrinsic measures. The first method is a rule-based one which uses information about the unique beginners in WordNet to classify NPs on the basis of their animacy. The second method relies on a machine learning algorithm which exploits a WordNet enriched with animacy information for each sense. The effect of word sense disambiguation on the two methods is also assessed. The intrinsic evaluation reveals that the machine learning method reaches human levels of performance. The extrinsic evaluation demonstrates that animacy identification can be beneficial in anaphora resolution, especially in the cases where animate entities are identified with high precision.

Machine Learning in Natural Language Processing

Handbook of Research on Machine Learning Applications and Trends ◽

10.4018/978-1-60566-766-9.ch014 ◽

2010 ◽

pp. 302-324

Author(s):

Marina Sokolova ◽

Stan Szpakowicz

Keyword(s):

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Text Processing ◽

Machine Learning Techniques ◽

Word Sense ◽

Part Of Speech ◽

Applications Of Machine Learning

This chapter presents applications of machine learning techniques to traditional problems in natural language processing, including part-of-speech tagging, entity recognition and word-sense disambiguation. People usually solve such problems without difficulty or at least do a very good job. Linguistics may suggest labour-intensive ways of manually constructing rule-based systems. It is, however, the easy availability of large collections of texts that has made machine learning a method of choice for processing volumes of data well above the human capacity. One of the main purposes of text processing is all manner of information extraction and knowledge extraction from such large text. Machine learning methods discussed in this chapter have stimulated wide-ranging research in natural language processing and helped build applications with serious deployment potential.