A Karaka Based Annotation Scheme for English

Vaidya, Ashwini; Husain, Samar; Mannem, Prashanth; Sharma, Dipti Misra

doi:10.1007/978-3-642-00382-0_4

Cited by 10 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Propbank label [ARG0] denotes the agent of the verb, the argument which causes the action, whereas [ARG1] denotes the argument which is affected or changed by the action. Paninian dependency labels don't account into unaccusativity and hence, k1 maps to both [ARG0] and [ARG1], subject to context (Vaidya et al, 2009(Vaidya et al, , 2011 The precision, recall and F1 scores for the various labels obtained in the Argument Classification step are given in Table 15. We see that [ARG2] and [ARG2_GOL] have a significantly low F1 score, although the precision values are decent.…”

Section: Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

A Dataset for Semantic Role Labelling of Hindi-English Code-Mixed Tweets

Pal¹,

Sharma

2019

Proceedings of the 13th Linguistic Annotation Workshop

View full text Add to dashboard Cite

We present a data set of 1460 Hindi-English code-mixed tweets consisting of 20,949 tokens labelled with Proposition Bank labels marking their semantic roles. We created verb frames for complex predicates present in the corpus and formulated mappings from Paninian dependency labels to Proposition Bank labels. With the help of these mappings and the dependency tree, we propose a baseline rule based system for Semantic Role Labelling of Hindi-English code-mixed data. We obtain an accuracy of 96.74% for Argument Identification and are able to further classify 73.93% of the labels correctly. While there is relevant ongoing research on Semantic Role Labelling (SRL) and on building tools for code-mixed social media data, this is the first attempt at labelling semantic roles in Hindi-English codemixed data, to the best of our knowledge.

show abstract

Section: Results and Analysismentioning

confidence: 99%

“…Research shows that English Propbank data is similar to English Dependency Treebank labelled with Paninan dependency labels. (Vaidya et al, 2009). We use these mappings (Table 11, Table 12) to create a rule based model for automatic annotation of semantic roles.…”

Section: Rule-based Approachmentioning

confidence: 99%

A Dataset for Semantic Role Labelling of Hindi-English Code-Mixed Tweets

Pal¹,

Sharma

2019

Proceedings of the 13th Linguistic Annotation Workshop

View full text Add to dashboard Cite

show abstract

“…The participant relations with the verb are called karaka. The notion of karaka will incorporate the 'local' semantics of the verb in a sentence, while also taking cue from the surface level morpho-syntactic information (Vaidya et al 2009). There are six basic karakas, namely;…”

Section: Paninian Grammatical Modelmentioning

confidence: 99%

Hindi CCGbank: A CCG treebank from the Hindi dependency treebank

Ambati

Deoskar

Steedman

2017

Lang Resources & Evaluation

View full text Add to dashboard Cite

In this paper, we present an approach for automatically creating a combinatory categorial grammar (CCG) treebank from a dependency treebank for the subject-object-verb language Hindi. Rather than a direct conversion from dependency trees to CCG trees, we propose a two stage approach: a language independent generic algorithm first extracts a CCG lexicon from the dependency treebank. An exhaustive CCG parser then creates a treebank of CCG derivations. We also discuss special cases of this generic algorithm to handle linguistic phenomena specific to Hindi. In doing so we extract different constructions with long-range dependencies like coordinate constructions and non-projective dependencies resulting from constructions like relative clauses, noun elaboration and verbal modifiers.

show abstract

“…All these three languages are morphologically rich and have a relatively free word order. The applicability of this scheme for syntactic annotation of a fixed word order language like English has also been studied to some extent [12], [13]. The annotation scheme is based on a grammatical formalism known as Computational Pan .…”

Section: Related Work and Backgroundmentioning

confidence: 99%

External Sandhi and its Relevance to Syntactic Treebanking

Kolachina¹,

Sharma²,

Gadde³

et al. 2011

Polibits

Self Cite

View full text Add to dashboard Cite

External sandhi is a linguistic phenomenon which refers to a set of sound changes that occur at word boundaries. These changes are similar to phonological processes such as assimilation and fusion when they apply at the level of prosody, such as in connected speech. External sandhi formation can be orthographically reflected in some languages. External sandhi formation in such languages, causes the occurrence of forms which are morphologically unanalyzable, thus posing a problem for all kind of NLP applications. In this paper, we discuss the implications that this phenomenon has for the syntactic annotation of sentences in Telugu, an Indian language with agglutinative morphology. We describe in detail, how external sandhi formation in Telugu, if not handled prior to dependency annotation, leads either to loss or misrepresentation of syntactic information in the treebank. This phenomenon, we argue, necessitates the introduction of a sandhi splitting stage in the generic annotation pipeline currently being followed for the treebanking of Indian languages. We identify one type of external sandhi widely occurring in the previous version of the Telugu treebank (version 0.2) and manually split all its instances leading to the development of a new version 0.5. We also conduct an experiment with a statistical parser to empirically verify the usefulness of the changes made to the treebank. Comparing the parsing accuracies obtained on versions 0.2 and 0.5 of the treebank, we observe that splitting even just one type of external sandhi leads to an increase in the overall parsing accuracies.

show abstract

A Karaka Based Annotation Scheme for English

Cited by 10 publications

References 5 publications

A Dataset for Semantic Role Labelling of Hindi-English Code-Mixed Tweets

A Dataset for Semantic Role Labelling of Hindi-English Code-Mixed Tweets

Hindi CCGbank: A CCG treebank from the Hindi dependency treebank

External Sandhi and its Relevance to Syntactic Treebanking

Contact Info

Product

Resources

About