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Twitter language identification of similar languages and dialects without ground truth

April 3, 2017
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Conference Paper
Author:
Jennifer A. Williams
…
Published in:
Proc. 4th Workshop on NLP for Similar Languages, Varieties and Dialects, 3 April 2017, pp. 73-83.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

We present a new method to bootstrap filter Twitter language ID labels in our dataset for automatic language identification (LID). Our method combines geolocation, original Twitter LID labels, and Amazon Mechanical Turk to resolve missing and unreliable labels. We are the first to compare LID classification performance using the MIRA algorithm and langid.py. We show classifier performance on different versions of our dataset with high accuracy using only Twitter data, without ground truth, and very few training examples. We also show how Platt Scaling can be use to calibrate MIRA classifier output values into a probability distribution over candidate classes, making the output more intuitive. Our method allows for fine-grained distinctions between similar languages and dialects and allows us to rediscover the language composition of our Twitter dataset.
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Summary

We present a new method to bootstrap filter Twitter language ID labels in our dataset for automatic language identification (LID). Our method combines geolocation, original Twitter LID labels, and Amazon Mechanical Turk to resolve missing and unreliable labels. We are the first to compare LID classification performance using the MIRA...

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Twitter language identification of similar languages and dialects without ground truth

LLTools: machine learning for human language processing

December 5, 2016
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Conference Paper
Author:
Cagri K. Dagli
…
Published in:
30th Conf. on Neural Info. Processing Syst., NIPS 2016, 5-10 December 2016.
Topic:
big data
R&D area:
R&D group:

Summary

Machine learning methods in Human Language Technology have reached a stage of maturity where widespread use is both possible and desirable. The MIT Lincoln Laboratory LLTools software suite provides a step towards this goal by providing a set of easily accessible frameworks for incorporating speech, text, and entity resolution components into larger applications. For the speech processing component, the pySLGR (Speaker, Language, Gender Recognition) tool provides signal processing, standard feature analysis, speech utterance embedding, and machine learning modeling methods in Python. The text processing component in LLTools extracts semantically meaningful insights from unstructured data via entity extraction, topic modeling, and document classification. The entity resolution component in LLTools provides approximate string matching, author recognition and graph-based methods for identifying and linking different instances of the same real-world entity. We show through two applications that LLTools can be used to rapidly create and train research prototypes for human language processing.
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Summary

Machine learning methods in Human Language Technology have reached a stage of maturity where widespread use is both possible and desirable. The MIT Lincoln Laboratory LLTools software suite provides a step towards this goal by providing a set of easily accessible frameworks for incorporating speech, text, and entity resolution components...

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LLTools: machine learning for human language processing

Finding good enough: a task-based evaluation of query biased summarization for cross language information retrieval

October 25, 2014
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Conference Paper
Author:
Jennifer A. Williams
…
Published in:
EMNLP 2014, Proc. of Conf. on Empirical Methods in Natural Language Processing, 25-29 October, 2014, pp. 657-69.
Topic:
language recognition
R&D area:
R&D group:

Summary

In this paper we present our task-based evaluation of query biased summarization for cross-language information retrieval (CLIR) using relevance prediction. We describe our 13 summarization methods each from one of four summarization strategies. We show how well our methods perform using Farsi text from the CLEF 2008 shared-task, which we translated to English automatically. We report precision/recall/F1, accuracy and time-on-task. We found that different summarization methods perform optimally for different evaluation metrics, but overall query biased word clouds are the best summarization strategy. In our analysis, we demonstrate that using the ROUGE metric on our sentence-based summaries cannot make the same kinds of distinctions as our evaluation framework does. Finally, we present our recommendations for creating much-needed evaluation standards and databases.
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Summary

In this paper we present our task-based evaluation of query biased summarization for cross-language information retrieval (CLIR) using relevance prediction. We describe our 13 summarization methods each from one of four summarization strategies. We show how well our methods perform using Farsi text from the CLEF 2008 shared-task, which we...

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Finding good enough: a task-based evaluation of query biased summarization for cross language information retrieval

A language-independent approach to automatic text difficulty assessment for second-language learners

August 4, 2013
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Conference Paper
Author:
Wade Shen
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Published in:
Proc. 2nd Workshop on Predicting and Improving Text Readability for Target Reader Populations, 4-9 August 2013.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

In this paper we introduce a new baseline for language-independent text difficulty assessment applied to the Interagency Language Roundtable (ILR) proficiency scale. We demonstrate that reading level assessment is a discriminative problem that is best-suited for regression. Our baseline uses z-normalized shallow length features and TF-LOG weighted vectors on bag-of-words for Arabic, Dari, English, and Pashto. We compare Support Vector Machines and the Margin-Infused Relaxed Algorithm measured by mean squared error. We provide an analysis of which features are most predictive of a given level.
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Summary

In this paper we introduce a new baseline for language-independent text difficulty assessment applied to the Interagency Language Roundtable (ILR) proficiency scale. We demonstrate that reading level assessment is a discriminative problem that is best-suited for regression. Our baseline uses z-normalized shallow length features and TF-LOG weighted vectors on bag-of-words...

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A language-independent approach to automatic text difficulty assessment for second-language learners
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