Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit

Krishna, Amrith; Santra, Bishal; Bandaru, Sasi Prasanth; Sahu, Gaurav; Sharma, V. D.; Satuluri, Pavankumar; Goyal, Pawan

doi:10.18653/v1/d18-1276

Cited by 8 publications

(30 citation statements)

References 38 publications

(50 reference statements)

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“…More contextual features and different tagger architectures have been found to be much better for tagging languages that lack articles like the and languages with freer word order than English, e.g. Sanskrit [28] or Turkish [29]. For language of people with neurological deficits, similar such alternative taggers these might classify parts of speech more like a human expert than the present system does.…”

Section: Future Workmentioning

confidence: 99%

A "Verbal Thermometer" for Assessing Neurodegenerative Disease: Automated Measurement of Pronoun and Verb Ratio from Speech

Jarrold

Rofes

Wilson

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Clinicians often use speech to characterize neurodegenerative disorders. Such characterizations require clinical judgment, which is subjective and can require extensive training. Quantitative Production Analysis (QPA) can be used to obtain objective quantifiable assessments of patient functioning. However, such human-based analyses of speech are costly and time consuming. Inexpensive off-the-shelf technologies such as speech recognition and part of speech taggers may avoid these problems. This study evaluates the ability of an automatic speech to text transcription system and a part of speech tagger to assist with measuring pronoun and verb ratios, measures based on QPA. Five participant groups provided spontaneous speech samples. One group consisted of healthy controls, while the remaining groups represented four subtypes of frontotemporal dementia. Findings indicated measurement of pronoun and verb ratio was robust despite errors introduced by automatic transcription and the tagger and despite these off-the-shelf products not having been trained on the language obtained from speech of the included population. Clinical relevance Linguistic differences in individuals with neurodegenerative disease can be subtle. Automated measurement of pronoun and verb ratio (PR and VR) showed promise despite the early stage of pipeline development using off-theshelf tools. Intraclass correlations between automatic versus human measures of PR and VR were in the moderate to excellent range as measured by intra-class correlation.

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Section: Future Workmentioning

confidence: 99%

A "Verbal Thermometer" for Assessing Neurodegenerative Disease: Automated Measurement of Pronoun and Verb Ratio from Speech

Jarrold

Rofes

Wilson

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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“…In MRLs, hand-crafted features still form a crucial component in contributing to the performance of the state-of-the-art systems for tasks such as morphological parsing and dependency parsing More et al, 2019;Seeker and Ç etinoglu 2015). But Krishna et al (2018) learn a feature function using the Path Ranking Algorithm (PRA) (Lao and Cohen 2010) for the joint task of word segmentation and morphological parsing. PRA essentially maps the problem of learning a feature function to that of automatic learning of horn clauses (Gardner, Talukdar, and Mitchell 2015), where each clause is a morphological constraint.…”

Section: Figurementioning

confidence: 99%

“…The domain knowledge required here confines to just defining the literals, the combinations of which will be used to form the clauses. In Krishna et al (2018), morphological tags and grammatical categories form the literals and the feature (clause) values are calculated using distributional information from a morphologically tagged corpus. We find that the same feature function can be used effectively for all the standalone and joint tasks we experimented with, including the downstream morphosyntactic tasks.…”

Section: Figurementioning

confidence: 99%

“…However, the sentence will still maintain the same syntactic analysis, irrespective of its varying word orders (Scharf et al 2015;Gillon and Shaer 2005). Recently, Krishna et al (2018) have shown that approaches for non-sequential processing of Sanskrit sentences result in better system performance even for lowlevel tasks such as word-segmentation and morphological parsing. In this work, we extend the energy-based model (EBM) for joint modeling of word segmentation and morphological parsing proposed by Krishna et al (2018) into a general graph-based parsing framework for multiple structured prediction tasks in Sanskrit.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Krishna et al (2018) have shown that approaches for non-sequential processing of Sanskrit sentences result in better system performance even for lowlevel tasks such as word-segmentation and morphological parsing. In this work, we extend the energy-based model (EBM) for joint modeling of word segmentation and morphological parsing proposed by Krishna et al (2018) into a general graph-based parsing framework for multiple structured prediction tasks in Sanskrit. We extend the framework to include two downstream syntax-level tasks, dependency parsing and syntactic linearization.…”

Section: Introductionmentioning

confidence: 99%

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A Graph-Based Framework for Structured Prediction Tasks in Sanskrit

Krishna

Santra

Gupta

et al. 2021

Computational Linguistics

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We propose a framework using Energy Based Models for multiple structured prediction tasks in Sanskrit. Ours is an arc-factored model, similar to the graph based parsing approaches, and we consider the tasks of word-segmentation, morphological parsing, dependency parsing, syntactic linearisation and prosodification, a prosody level task we introduce in this work. Ours is a search based structured prediction framework, which expects a graph as input, where relevant linguistic information is encoded in the nodes, and the edges are then used to indicate the association between these nodes. Typically the state of the art models for morphosyntactic tasks in morphologically rich languages still rely on hand-crafted features for their performance. But here, we automate the learning of the feature function. The feature function so learnt along with the search space we construct, encode relevant linguistic information for the tasks we consider. This enables us to substantially reduce the training data requirements to as low as 10 % as compared to the data requirements for the neural state of the art models. Our experiments in Czech and Sanskrit show the language agnostic nature of the framework, where we train highly competitive models for both the languages. Moreover, our framework enables to incorporate language specific constraints to prune the search space and to filter the candidates during inference. We obtain significant improvements in morphosyntactic tasks for Sanskrit by incorporating language specific constraints into the model. In all the tasks we discuss for Sanskrit, we either achieve state of the art results or ours is the only data driven solution for those tasks.

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Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit

Krishna¹,

Gupta²,

Garasangi³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Morphologically rich languages seem to benefit from joint processing of morphology and syntax, as compared to pipeline architectures. We propose a graph-based model for joint morphological parsing and dependency parsing in Sanskrit. Here, we extend the Energy based model framework (Krishna et al., 2020), proposed for several structured prediction tasks in Sanskrit, in 2 simple yet significant ways. First, the framework's default input graph generation method is modified to generate a multigraph, which enables the use of an exact search inference. Second, we prune the input search space using a linguistically motivated approach, rooted in the traditional grammatical analysis of Sanskrit. Our experiments show that the morphological parsing from our joint model outperforms standalone morphological parsers. We report state of the art results in morphological parsing, and in dependency parsing, both in standalone (with gold morphological tags) and joint morphosyntactic parsing setting.

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Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit

Cited by 8 publications

References 38 publications

A "Verbal Thermometer" for Assessing Neurodegenerative Disease: Automated Measurement of Pronoun and Verb Ratio from Speech

A "Verbal Thermometer" for Assessing Neurodegenerative Disease: Automated Measurement of Pronoun and Verb Ratio from Speech

A Graph-Based Framework for Structured Prediction Tasks in Sanskrit

Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit

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