Joint Morphological and Syntactic Analysis for Richly Inflected                     Languages

Bohnet, Bernd; Nivre, Joakim; Boguslavsky, Igor; Farkas, Richárd; Ginter, Filip; Hajič, Jan

doi:10.1162/tacl_a_00238

Cited by 62 publications

(77 citation statements)

References 14 publications

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“…Preparatory for all modules is the natural language processing of the input text corpora. To this end, we utilize the NLP tool chain of TextImager [63] to carry out tokenization, sentence spli ing, part of speech tagging, lemmatization, morphological tagging, named entity recognition, dependency parsing [17] and automatic disambiguation -the la er by means of fastSense [137]. For more details on these submodules see [36,137].…”

Section: Module 1: Natural Languagementioning

confidence: 99%

From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information

et al. 2020

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Are nearby places (e.g. cities) described by related words? In this article we transfer this research question in the eld of lexical encoding of geographic information onto the level of intertextuality. To this end, we explore Volunteered Geographic Information (VGI) to model texts addressing places at the level of cities or regions with the help of so-called topic networks. is is done to examine how language encodes and networks geographic information on the aboutness level of texts. Our hypothesis is that the networked thematizations of places are similar -regardless of their distances and the underlying communities of authors. To investigate this we introduce Multiplex Topic Networks (MTN), which we automatically derive from Linguistic Multilayer Networks (LMN) as a novel model, especially of thematic networking in text corpora. Our study shows a Zip an organization of the thematic universe in which geographical places (especially cities) are located in online communication. We interpret this nding in the context of cognitive maps, a notion which we extend by so-called thematic maps. According to our interpretation of this nding, the organization of thematic maps as part of cognitive maps results from a tendency of authors to generate shareable content that ensures the continued existence of the underlying media. We test our hypothesis by example of special wikis and extracts of Wikipedia. In this way we come to the conclusion: Places, whether close to each other or not, are located in neighboring places that span similar subnetworks in the topic universe.

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Section: Module 1: Natural Languagementioning

confidence: 99%

From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information

et al. 2020

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“…Methods for joint morphological disambiguation and parsing have been widely explored Tsarfaty (2006;Cohen and Smith (2007;Goldberg and Tsarfaty (2008;Goldberg and Elhadad (2011). More recently, Bohnet et al (2013) presented an arc-standard transition-based parser that performs competitively for joint morphological tagging and dependency parsing for richly inflected languages, such as Czech, Finnish, German, Hungarian, and Russian. Our model seeks to achieve a similar benefit to parsing without explicitly reasoning about the internal structure of words.…”

Section: Related Workmentioning

confidence: 99%

Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs

Ballesteros

Dyer²,

Smith

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

217

180

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We present extensions to a continuousstate dependency parsing method that makes it applicable to morphologically rich languages. Starting with a highperformance transition-based parser that uses long short-term memory (LSTM) recurrent neural networks to learn representations of the parser state, we replace lookup-based word representations with representations constructed from the orthographic representations of the words, also using LSTMs. This allows statistical sharing across word forms that are similar on the surface. Experiments for morphologically rich languages show that the parsing model benefits from incorporating the character-based encodings of words.

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“…Stanford CoreNLP tools (Manning et al, 2014) are used for tokenisations and POS-tagging of the input. Using a shallow parser (Bohnet et al, 2013) we obtain the dependency relations for the sentences. Our ASP representation contains atoms of the following form: pos (c_NNP, 1 ) .…”

Section: Preprocessingmentioning

confidence: 99%

Improving scalability of inductive logic programming via pruning and best-effort optimisation

Kazmi

Schüller

Saygın

2017

Expert Systems with Applications

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Inductive Logic Programming (ILP) combines rule-based and statistical artificial intelligence methods, by learning a hypothesis comprising a set of rules given background knowledge and constraints for the search space. We focus on extending the XHAIL algorithm for ILP which is based on Answer Set Programming and we evaluate our extensions using the Natural Language Processing application of sentence chunking. With respect to processing natural language, ILP can cater for the constant change in how we use language on a daily basis. At the same time, ILP does not require huge amounts of training examples such as other statistical methods and produces interpretable results, that means a set of rules, which can be analysed and tweaked if necessary. As contributions we extend XHAIL with (i) a pruning mechanism within the hypothesis generalisation algorithm which enables learning from larger datasets, (ii) a better usage of modern solver technology using recently developed optimisation methods, and (iii) a time budget that permits the usage of suboptimal results. We evaluate these improvements on the task of sentence chunking using three datasets from a recent SemEval competition. Results show that our improvements allow for learning on bigger datasets with results that are of similar quality to state-of-the-art systems on the same task. Moreover, we compare the hypotheses obtained on datasets to gain insights on the structure of each dataset.

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Joint Morphological and Syntactic Analysis for Richly Inflected Languages

Cited by 62 publications

References 14 publications

From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information

From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information

Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs

Improving scalability of inductive logic programming via pruning and best-effort optimisation

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