Identifying Various Kinds of Event Mentions in K-Parser Output

Sharma, Arpit; Vo, Nguyen; Aditya, Somak; Baral, Chitta

doi:10.3115/v1/w15-0811

Cited by 6 publications

(9 citation statements)

References 15 publications

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“…For semantic analysis, in absence of clear approaches for quantification, we observe the output of a knowledge parser, K-Parser (Sharma et al 2015), and report most frequent entities and their semantic roles (Palmer, Gildea, and Kingsbury 2005).…”

Section: Authors' Style Analysismentioning

confidence: 99%

A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text

Verma,

Srinivasan

2019

Preprint

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With a growing interest in modeling inherent subjectivity in natural language, we present a linguistically-motivated process to understand and analyze the writing style of individuals from three perspectives: lexical, syntactic, and semantic. We discuss the stylistically expressive elements within each of these levels and use existing methods to quantify the linguistic intuitions related to some of these elements. We show that such a multi-level analysis is useful for developing a wellknit understanding of style -which is independent of the natural language task at hand, and also demonstrate its value in solving three downstream tasks: authors' style analysis, authorship attribution, and emotion prediction. We conduct experiments on a variety of datasets, comprising texts from social networking sites, user reviews, legal documents, literary books, and newswire. The results on the aforementioned tasks and datasets illustrate that such a multi-level understanding of style, which has been largely ignored in recent works, models style-related subjectivity in text and can be leveraged to improve performance on multiple downstream tasks both qualitatively and quantitatively.

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Section: Authors' Style Analysismentioning

confidence: 99%

A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text

Verma,

Srinivasan

2019

Preprint

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“…The automatic development of such representations boils down to the well known complex problem of translating a natural language text into its formal meaning representation. Among these works, only the work of (Sharma et al, 2015b) accepts natural language knowledge sentences which it automatically converts into their required graphical representation (Sharma et al, 2015a). The remaining two (Bailey et al, 2015;Schüller, 2014) requires the knowledge to be provided in a logical form.…”

Section: Related Workmentioning

confidence: 99%

Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge

Prakash¹,

Sharma²,

Mitra³

et al. 2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge. The needed knowledge is not present in the given text. Automatic extraction of the needed knowledge is a bottleneck in solving the challenge. The existing state-of-the-art approach uses the knowledge embedded in their pretrained language model. However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts. This limits the performance of such models on the WSC problems. In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module. Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53% and 7.7% respectively. Overall our system achieves the state-of-theart accuracy of 71.06% on the WSC dataset, an improvement of 7.36% over the previous best.

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“…Graphical meaning representations are popular for natural languages such as English. It is because of their simplicity, readability and ability to be easily processed, that in the recent years there has been a significant amount of progress (Sharma et al 2015a;Banarescu et al 2013) in defining graphical representations for natural language and development of systems which can automatically parse a natural language text into those representations. Inspired by such representations, in this work we use a graphical schema to represent the sentences in a WSC problem, and a piece of knowledge.…”

Section: Graphical Representation Of a Wsc Problemmentioning

confidence: 99%

“…All the edges labels other than instance of part of a predefined set of binary relations between two nodes (as mentioned in the Definition 4). In this work, these relations are from the relations in a semantic parser called K-Parser (Sharma et al 2015a).…”

Section: Graphical Representation Of a Wsc Problemmentioning

confidence: 99%

Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge

Sharma¹

2019

Theory and Practice of Logic Programming

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The Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011. In this work we attempt to solve WSC problems by reasoning with additional knowledge. By using an approach built on top of graph-subgraph isomorphism encoded using Answer Set Programming (ASP) we were able to handle 240 out of 291 WSC problems. The ASP encoding allows us to add additional constraints in an elaboration tolerant manner. In the process we present a graph based representation of WSC problems as well as relevant commonsense knowledge.

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Identifying Various Kinds of Event Mentions in K-Parser Output

Cited by 6 publications

References 15 publications

A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text

A Lexical, Syntactic, and Semantic Perspective for Understanding Style in Text

Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge

Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge

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