scholarly journals Low-Resource Active Learning of North Sámi Morphological Segmentation

2015 ◽  
pp. 20 ◽  
Author(s):  
Stig-Arne Grönroos ◽  
Kristiina Jokinen ◽  
Katri Hiovain ◽  
Mikko Kurimo ◽  
Sami Virpioja
Keyword(s):  
Active Learning ◽  
Statistical Learning ◽  
Language Processing ◽  
Morphological Analysis ◽  
Morphological Structure ◽  
Data Driven ◽  
Learning Approach ◽  
High Quality ◽  
Morphological Segmentation ◽  
Word Forms

Many Uralic languages have a rich morphological structure, but lack tools of morphological analysis needed for efficient language processing. While creating a high-quality morphological analyzer requires a significant amount of expert labor, data-driven approaches may provide sufficient quality for many applications.We study how to create a statistical model for morphological segmentation of North Sámi language with a large unannotated corpus and a small amount of human-annotated word forms selected using an active learning approach. For statistical learning, we use the semi-supervised Morfessor Baseline and FlatCat methods. Aer annotating 237 words with our active learning setup, we improve morph boundary recall over 20% with no loss of precision.

scholarly journals Low-Resource Active Learning of Morphological Segmentation

2016 ◽  
Vol 4 ◽  
pp. 47-72
Author(s):  
Stig-Arne Grönroos ◽  
Katri Hiovain ◽  
Peter Smit ◽  
Ilona Rauhala ◽  
Kristiina Jokinen ◽  
...  
Keyword(s):  
Active Learning ◽  
Language Processing ◽  
Morphological Analysis ◽  
Morphological Structure ◽  
Data Driven ◽  
Learning Approach ◽  
Morphological Segmentation ◽  
Word Forms ◽  
Set Up ◽  
Simulated Scenario

Many Uralic languages have a rich morphological structure, but lack morphological analysis tools needed for efficient language processing. While creating a high-quality morphological analyzer requires a significant amount of expert labor, data-driven approaches may provide sufficient quality for many applications. We study how to create a statistical model for morphological segmentation with a large unannotated corpus and a small amount of annotated word forms selected using an active learning approach. We apply the procedure to two Finno-Ugric languages: Finnish and North Sámi. The semi-supervised Morfessor FlatCat method is used for statistical learning. For Finnish, we set up a simulated scenario to test various active learning query strategies. The best performance is provided by a coverage-based strategy on word initial and final substrings. For North Sámi we collect a set of humanannotated data. With 300 words annotated with our active learning setup, we see a relative improvement in morph boundary F1-score of 19% compared to unsupervised learning and 7.8% compared to random selection.

scholarly journals A Machine Learning Approach to Modeling Scope Preferences

2003 ◽  
Vol 29 (1) ◽  
pp. 73-96 ◽  
Author(s):  
Derrick Higgins ◽  
Jerrold M. Sadock
Keyword(s):  
Language Processing ◽  
Data Driven ◽  
Learning Approach ◽  
Quantifier Scope ◽  
Multiple Sources ◽  
Machine Learning Approach ◽  
Theoretical Linguistics ◽  
History Of ◽  
Scope Reading ◽  
Syntactic Annotation

This article describes a corpus-based investigation of quantifier scope preferences. Following recent work on multimodular grammar frameworks in theoretical linguistics and a long history of combining multiple information sources in natural language processing, scope is treated as a distinct module of grammar from syntax. This module incorporates multiple sources of evidence regarding the most likely scope reading for a sentence and is entirely data-driven. The experiments discussed in this article evaluate the performance of our models in predicting the most likely scope reading for a particular sentence, using Penn Treebank data both with and without syntactic annotation. We wish to focus attention on the issue of determining scope preferences, which has largely been ignored in theoretical linguistics, and to explore different models of the interaction between syntax and quantifier scope.

scholarly journals An Algorithm for Morphological Segmentation of Esperanto Words

2016 ◽  
Vol 105 (1) ◽  
pp. 63-76
Author(s):  
Theresa Guinard
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Morphological Analysis ◽  
Compound Words ◽  
Part Of Speech ◽  
Semantic Classes ◽  
Morphological Segmentation ◽  
Segmentation Accuracy ◽  
N Gram ◽  
Speech Information

Abstract Morphological analysis (finding the component morphemes of a word and tagging morphemes with part-of-speech information) is a useful preprocessing step in many natural language processing applications, especially for synthetic languages. Compound words from the constructed language Esperanto are formed by straightforward agglutination, but for many words, there is more than one possible sequence of component morphemes. However, one segmentation is usually more semantically probable than the others. This paper presents a modified n-gram Markov model that finds the most probable segmentation of any Esperanto word, where the model’s states represent morpheme part-of-speech and semantic classes. The overall segmentation accuracy was over 98% for a set of presegmented dictionary words.

scholarly journals Dependency Parsing of Turkish

2008 ◽  
Vol 34 (3) ◽  
pp. 357-389 ◽  
Author(s):  
Gülşen Eryiğit ◽  
Joakim Nivre ◽  
Kemal Oflazer
Keyword(s):  
Morphological Structure ◽  
Data Driven ◽  
Syntactic Parsing ◽  
Dependency Parsing ◽  
Beam Search ◽  
Lexical Information ◽  
Order Language ◽  
Word Forms ◽  
Syntactic Relations ◽  
The Impact

The suitability of different parsing methods for different languages is an important topic in syntactic parsing. Especially lesser-studied languages, typologically different from the languages for which methods have originally been developed, pose interesting challenges in this respect. This article presents an investigation of data-driven dependency parsing of Turkish, an agglutinative, free constituent order language that can be seen as the representative of a wider class of languages of similar type. Our investigations show that morphological structure plays an essential role in finding syntactic relations in such a language. In particular, we show that employing sublexical units called inflectional groups, rather than word forms, as the basic parsing units improves parsing accuracy. We test our claim on two different parsing methods, one based on a probabilistic model with beam search and the other based on discriminative classifiers and a deterministic parsing strategy, and show that the usefulness of sublexical units holds regardless of the parsing method. We examine the impact of morphological and lexical information in detail and show that, properly used, this kind of information can improve parsing accuracy substantially. Applying the techniques presented in this article, we achieve the highest reported accuracy for parsing the Turkish Treebank.

Using Pre-trained Language Model to Enhance Active Learning for Sentence Matching

2022 ◽  
Vol 21 (2) ◽  
pp. 1-19
Author(s):  
Guirong Bai ◽  
Shizhu He ◽  
Kang Liu ◽  
Jun Zhao
Keyword(s):  
Active Learning ◽  
Language Model ◽  
Experimental Results ◽  
Language Models ◽  
Data Driven ◽  
Learning Approach ◽  
Sentence Matching ◽  
Learning Language

Active learning is an effective method to substantially alleviate the problem of expensive annotation cost for data-driven models. Recently, pre-trained language models have been demonstrated to be powerful for learning language representations. In this article, we demonstrate that the pre-trained language model can also utilize its learned textual characteristics to enrich criteria of active learning. Specifically, we provide extra textual criteria with the pre-trained language model to measure instances, including noise, coverage, and diversity. With these extra textual criteria, we can select more efficient instances for annotation and obtain better results. We conduct experiments on both English and Chinese sentence matching datasets. The experimental results show that the proposed active learning approach can be enhanced by the pre-trained language model and obtain better performance.

scholarly journals Manipuri Morphological Analysis

2020 ◽  
Vol 9 (2) ◽  
pp. 4-10
Author(s):  
Y. Bablu Singh ◽  
Th. Mamata Devi ◽  
Ch. Yashawanta Singh
Keyword(s):  
Computational Linguistics ◽  
Language Processing ◽  
Morphological Analysis ◽  
Building Blocks ◽  
Automatic Indexing ◽  
Standard Format ◽  
Computational Morphology ◽  
Word Forms ◽  
Computer Based ◽  
Feature Values

Morphological analysis is the basic foundation in Natural Language Processing applications including Syntax Parsing, Machine Translation (MT), Information Retrieval (IR) and Automatic Indexing. Morphological Analysis can provide valuable information for computer based linguistics task such as Lemmatization and studies of internal structure of the words or the feature values of the word. Computational Morphology is the application of morphological rules in the field of Computational Linguistics, and it is the emerging area in AI, which studies the structure of words, which are formed by combining smaller units of linguistics information, called morphemes: the building blocks of words. It provides about Semantic and Syntactic role in a sentence. It can analyze the Manipuri word forms and produces grammatical information, which is associated with the lexicon. Morphological Analyzer for Manipuri language has been tested on 4500 Manipuri lexicons in Shakti Standard Format (SSF) using Meitei Mayek Unicode as source; thereby an accuracy of 84% has been obtained on a manual check.

scholarly journals Building neural network models for morphological and morpheme analysis of texts

2021 ◽  
Vol 33 (4) ◽  
pp. 117-130
Author(s):  
Alexander Sergeevich Sapin
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
High Performance ◽  
Morphological Analysis ◽  
Morphological Characteristics ◽  
Network Models ◽  
Word Form ◽  
Neural Network Models ◽  
Russian Word ◽  
Word Forms

Morphological analysis of text is one of the most important stages of natural language processing (NLP). Traditional and well-studied problems of morphological analysis include normalization (lemmatization) of a given word form, recognition of its morphological characteristics and their morphological disambiguation. The morphological analysis also involves the problem of morpheme segmentation of words (i.e., segmentation of words into constituent morphs and their classification), which is actual in some NLP applications. In recent years, several machine learning models have been developed, which increase the accuracy of traditional morphological analysis and morpheme segmentation, but performance of such models is insufficient for many applied problems. For morpheme segmentation, high-precision models have been built only for lemmas (normalized word forms). This paper describes two new high-accuracy neural network models that implement morphemic segmentation of Russian word forms with sufficiently high performance. The first model is based on convolutional neural networks and shows the state-of-the-art quality of morphemic segmentation for Russian word forms. The second model, besides morpheme segmentation of a word form, preliminarily refines its morphological characteristics, thereby performing their disambiguation. The performance of this joined morphological model is the best among the considered morpheme segmentation models, with comparable accuracy of segmentation.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

An Expert Data-Driven Air Combat Maneuver Model Learning Approach

2021 ◽  
Author(s):  
Su-Jeong Park ◽  
Soon-Seo Park ◽  
Han-Lim Choi ◽  
Kyeong-Soo An ◽  
Young-Gon Kim
Keyword(s):  
Data Driven ◽  
Learning Approach ◽  
Model Learning ◽  
Air Combat ◽  
Expert Data

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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