The Penn Treebank: An Overview

Taylor, Ann; Marcus, Mitchell P.; Santorini, Beatrice

doi:10.1007/978-94-010-0201-1_1

Cited by 301 publications

(160 citation statements)

References 3 publications

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“…In English language, totally, eight POS [7] are available and are given as follows: Noun, pronoun, adjective, verb, adverb, article, participles, and auxiliaries. Prediction of the possible occurrence of the next word from an existing entry of a word can also make possible with the help of information about POS in a sentence.…”

Section: Pos Taggingmentioning

confidence: 99%

Working of Acontext-Aware Conversational Entity

Shrivastava

Kumar

2017

Asian J Pharm Clin Res

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Introduction of new technologies into the world is increasing rapidly, and to assist the users to get equipped with such technologies, industries are providing customer care services. Contacting a customer care service is subjective to several overheads of selecting options from a listed set, waiting for the switching between selections, and awaiting the support of a customer care executive as the process usually requires a human intervention. Hence, a substitute for personnel is required by the IT industries to automate the communication process in assisting the customers. Chatbots with context aware question-answering capabilities can be viewed as a good solution to such customer care assistance. Development of a chatbot and the complexities involved in getting it to work effectively is delineated in this paper.

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Section: Pos Taggingmentioning

confidence: 99%

Working of Acontext-Aware Conversational Entity

Shrivastava

Kumar

2017

Asian J Pharm Clin Res

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“…It involves recognition, removal of errors and inconsistency to improve the quality of the dataset prior to the process of analysis [9]. The tweets were cleaned of irrelevant data to improve their quality.…”

Section: Data Cleaningmentioning

confidence: 99%

Twitter Sentiment Analysis on E-commerce Websites in India

Jhaveri¹,

Chaudhari²,

Kurup³

2015

IJCA

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In today's era, social media has become a valuable source of information, where people express their opinions. Analysis of such opinion-related data can provide productive insights. When these opinions are relevant to a company, accurate analysis can provide them with information like product quality, influencers affecting other customer decisions, early feedback on newly launched products, company news, trends and also knowledge about their competitors. Hence, harnessing and extracting insights from these sentiments is necessary for these companies to implement effective marketing strategies and better customer service. Carrying the same notion forward, we decided to extract sentiments from Twitter relevant to two e-commerce giants in India, Flipkart and Snapdeal. In this paper, various lexicon based approaches are applied and their accuracy is investigated.

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“…ME and OE The Penn-Helsinki Parsed Corpus of Middle English (PPCME2) 1 and the York-TorontoHelsinki Parsed Corpus of Old English Prose (Taylor et al, 2003b, YCOE) use a variant of the PTB annotation schema (Taylor et al, 2003a). YCOE contains the full West Saxon Gospel, but PPCME2 contains only a small fragment of a Wycliffite gospel of John, the ME data is thus complemented with parts of Genesis (G) and Numbers (N).…”

Section: Languages and Corpus Datamentioning

confidence: 99%

Diachronic proximity vs. data sparsity in cross-lingual parser projection. A case study on Germanic

Sukhareva¹,

Chiarcos²

2014

Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects

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For the study of historical language varieties, the sparsity of training data imposes immense problems on syntactic annotation and the development of NLP tools that automatize the process. In this paper, we explore strategies to compensate the lack of training data by including data from related varieties in a series of annotation projection experiments from English to four old Germanic languages: On dependency syntax projected from English to one or multiple language(s), we train a fragment-aware parser trained and apply it to the target language. For parser training, we consider small datasets from the target language as a baseline, and compare it with models trained on larger datasets from multiple varieties with different degrees of relatedness, thereby balancing sparsity and diachronic proximity.Our experiments show (a) that including related language data to training data in the target language can improve parsing performance, (b) that a parser trained on data from two related languages (and none from the target language)can reach a performance that is statistically not significantly worse than that of a parser trained on the projections to the target language, and (c) that both conclusions holds only among the three most closely related languages under consideration, but not necessarily the fourth.The experiments motivate the compilation of a larger parallel corpus of historical Germanic varieties as a basis for subsequent studies.

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The Penn Treebank: An Overview

Cited by 301 publications

References 3 publications

Working of Acontext-Aware Conversational Entity

Working of Acontext-Aware Conversational Entity

Twitter Sentiment Analysis on E-commerce Websites in India

Diachronic proximity vs. data sparsity in cross-lingual parser projection. A case study on Germanic

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