The Penn Treebank

Marcus, Mitchell P.; Kim, Grace; Marcinkiewicz, Mary Ann; MacIntyre, Robert; Bies, Ann; Ferguson, Mark; Katz, Karen; Schasberger, Britta

doi:10.3115/1075812.1075835

Cited by 414 publications

(61 citation statements)

References 9 publications

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“…Using simple retrieval and alignment operations, the model takes advantage of the statistics of word use. Unlike existing work (7,8,10), it need make no a priori commitment to particular grammars, heuristics, or sets of semantic roles, and it does not require an annotated corpus on which to train.…”

Section: Resultsmentioning

confidence: 99%

An unsupervised method for the extraction of propositional information from text

Dennis

2004

Proc. Natl. Acad. Sci. U.S.A.

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T he closely related fields of question answering and information extraction aim to search large databases of textual material (textbases) to find specific information required by the user (1, 2). As opposed to information retrieval systems, which attempt to identify relevant documents that discuss the topic of the user's information need, information extraction systems return specific information such as names, dates, or amounts that the user requests. Although information retrieval systems (such as Google and Alta Vista) are now in widespread commercial use, information extraction is a much more difficult task and, with some notable exceptions, most current systems are research prototypes. However, the potential significance of reliable information extraction systems is substantial. In military, scientific, and business intelligence gathering, being able to identify specific entities and resources of relevance across documents is crucial. Furthermore, some current information extraction systems now attempt the even more difficult task of providing summaries of relevant information compiled across a document set.The majority of current information extraction systems are based on surface analysis of text applied to very large textbases. Whereas the dominant approaches in the late 1980s and early 1990s would attempt deep linguistic analysis, proposition extraction, and reasoning, most current systems look for answer patterns within the raw text and apply simple heuristics to extract relevant information (3). Such approaches have been shown to work well when information is represented redundantly in the textbase and when the type of the answer is unambiguously specified by the question and tends to be unique within a given sentence or sentence fragment. Although these conditions often hold for general knowledge questions of the kind found in the Text REtrieval Conference (TREC) Question Answer track, there are many intelligence applications for which they cannot be guaranteed. Often relevant information will be stated only once or may only be inferred and never stated explicitly. Furthermore, the results of the most recent TREC question-answer competition suggest that deep reasoning systems may now have reached a level of sophistication that allows them to surpass the performance possible using surface-based approaches. In the 2002 TREC competition, the POWER ANSWER system (4), which converts both questions and answers into propositional form and uses an inference engine, achieved a confidence weighted score of 0.856, a substantive improvement over the second placed exactanswer (5), which received a score of 0.691 in the main questionanswering task.A key component in the performance of the POWER ANSWER system is its use of the WORDNET lexical database (6). WORDNET provides a catalog of simple relationships among words, such as synonymy, hypernymy, and part-of relations that POWER ANSWER uses to supplement its inference system. Despite the relatively small number of relations considered and the difficulties in achi...

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Section: Resultsmentioning

confidence: 99%

An unsupervised method for the extraction of propositional information from text

Dennis

2004

Proc. Natl. Acad. Sci. U.S.A.

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“…7 (Mitkov, 2005) (Marcus et al, 1999) Typically of more interest to Comp Ling, however, is the bottom of the page, as seen in Figure 2: the customer review section.…”

Section: Amazonmentioning

confidence: 99%

“…32 28 (Marcus et al, 1999) 29 (Mitkov, 2005) 30 (Bird, Loper, & Klein, 2009) 31 (Klein & Manning, 2003) 32 Tree generated using (Klein & Manning, 2003) 13 …”

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The Linguistics of Sentiment Analysis

Hart¹

2013

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“…For the PENN Treebank, the third version was available in 1999 (Marcus et al, 1999). Actually, this treebank is also involved in semantics research or in the annotation of entities and events (Song et al, 2015).…”

Section: Dependency Treebanksmentioning

confidence: 99%

A Multiform Balanced Dependency Treebank for Romanian

Colhon¹,

Mărănduc²,

Mititelu³

2017

Proceedings of the Workshop Knowledge Resources for the Socio-Economic Sciences and Humanities

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The UAIC-RoDia-DepTb is a balanced treebank, containing texts in non-standard language: 2,575 chats sentences, old Romanian texts (a Gospel printed in 1648, a codex of laws printed in 1818, a novel written in 1910), regional popular poetry, legal texts, Romanian and foreign fiction, quotations. The proportions are comparable; each of these types of texts is represented by subsets of at least 1,000 phrases, so that the parser can be trained on their peculiarities. The annotation of the treebank started in 2007, and it has classical tags, such as those in school grammar, with the intention of using the resource for didactic purposes. The classification of circumstantial modifiers is rich in semantic information. We present in this paper the development in progress of this resource which has been automatically annotated and entirely manually corrected. We try to add new texts, and to make it available in more formats, by keeping all the morphological and syntactic information annotated, and adding logicalsemantic information. We will describe here two conversions, from the classic syntactic format into Universal Dependencies format and into a logical-semantic layer, which will be shortly presented.

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The Penn Treebank

Cited by 414 publications

References 9 publications

An unsupervised method for the extraction of propositional information from text

An unsupervised method for the extraction of propositional information from text

The Linguistics of Sentiment Analysis

A Multiform Balanced Dependency Treebank for Romanian

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