The Stanford typed dependencies representation

Abstract. We introduce a new semantic annotation scheme for the Recognizing Textual Entailment (RTE) dataset as well as a manually annotated dataset that uses this scheme. The scheme addresses three types of modification that license entailment patterns: restrictive, appositive and conjunctive, with a formal semantic specification of these patterns' contribution for establishing entailment. These inferential constructions were found to occur in 77.68% of the entailments in the RTE 1-3 corpora. They were annotated with cross-annotator agreement of 70.73% on average. A central aim of our annotations is to examine components that address these phenomena in RTE systems. Specifically, the new annotated dataset is used for examining a syntactic rule base within the BIUTEE recognizer, a publicly available entailment system. According to our tests, the rule base is rarely used to process the phenomena annotated in our corpus and most of the recognition work is done by other components in the system. 3

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“…The rules in the system are defined based on dependency structures [23]; in Table 4 several of these rules are illustrated in text.…”

Section: The Biutee Systemmentioning

confidence: 99%

Semantic Annotation for Textual Entailment Recognition

Toledo

Katrenko

Alexandropoulou

et al. 2013

Advances in Computational Intelligence

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“…To identify simple sentences from the input text we have taken the help of openly available Stanford Parser 1 and Stanford CoreNLP 2 along with the help of Stanford Typed Dependency Manual (Marneffe & Manning, 2008). The Stanford Parser provides the dependency parsing of an input sentence and Stanford Deterministic Co-reference Resolution System which is a module of CoreNLP Suit helps us to solve the co-reference problem.…”

Section: Simple Sentence Identificationmentioning

confidence: 99%

“…If a sentence contains more than one nsubj or nsubjpass then it is considered as compound or complex. The nsubj and nsubjpass are categorized as subject according to Stanford Typed Dependency Manual (Marneffe & Manning, 2008 A simple sentence is build up of one independent clause whereas a complex or compound sentence is built from at least two clauses. From the sample sentences, we have got the Stanford Typed Dependency notations as shown in Table 1.…”

Section: Simple Sentence Identificationmentioning

confidence: 99%

Factual open cloze question generation for assessment of learner’s knowledge

Das

Majumder

2017

Int J Educ Technol High Educ

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Factual objective type questions are effectively used in active learning, information and communication technology based education and intelligent tutoring system for the assessment of learner's content knowledge. In this paper, we have presented an automatic factual open cloze question generation system which can generate fill-in-the-blank questions without alternatives. In order to generate the questions, the system first extracts a set of informative sentences from the given input corpus. The sentences are considered as informative based on part-of-speech tags and certain rules. After the identification of the informative sentences the questions are generated by omitting the answer-keys which are selected by identifying domain specific words in the sentences. The unbound option set of an open cloze question often confuses the examinees. However, open cloze questions require more productive knowledge from learners than cloze questions. Finally, we have also suggested answer hints for the examinees to reduce the number of possible answers that make assessment easier.

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“…The Stanford Parser produces 55 different typed dependencies [23]. These dependencies reflect the grammatical relationships between the words.…”

Section: Interaction By Commentsmentioning

confidence: 99%

Social Interaction for Interactive Storytelling

Lima

Feijó

Pozzer

et al. 2012

Lecture Notes in Computer Science

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Abstract. In recent years interactive narratives emerged as a new form of digital entertainment, allowing users to interact and change stories according to their own desires. In this paper, we explore the use of social networks as a way of interaction in interactive narratives. We present the interaction interface of an interactive storytelling system that allows users to interact and change stories through social networks. To validate our approach we conducted a user study with 24 participants. The preliminary results show that our method improved the user satisfaction and experience. The proposed methods can be adapted to other applications that require social interaction.

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The Stanford typed dependencies representation

Cited by 752 publications

References 23 publications

Semantic Annotation for Textual Entailment Recognition

Semantic Annotation for Textual Entailment Recognition

Factual open cloze question generation for assessment of learner’s knowledge

Social Interaction for Interactive Storytelling

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