Anaphors in Sanskrit

Proceedings of the Second Workshop on Anaphora Resolution (WAR II). Editor: Christer Johansson. NEALT Proceedings Series, Vol. 2 (2008), 11-25. © 2008 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/7129

Cross-Lingual Zero Pronoun Resolution

In languages like Arabic, Chinese, Italian, Japanese, Korean, Portuguese, Spanish, and many others, predicate arguments in certainsyntactic positions are not realized instead of being realized as overt pronouns, and are thus called zero- or null-pronouns. Identifyingand resolving such omitted arguments is crucial to machine translation, information extraction and other NLP tasks, but depends heavilyonsemanticcoherenceandlexicalrelationships. WeproposeaBERT-basedcross-lingualmodelforzeropronounresolution,andevaluateit on the Arabic and Chinese portions of OntoNotes 5.0. As far as we know, ours is the first neural model of zero-pronoun resolutionfor Arabic; and our model also outperforms the state-of-the-art for Chinese. In the paper we also evaluate BERT feature extraction andfine-tune models on the task, and compare them with our model. We also report on an investigation of BERT layers indicating whichlayer encodes the most suitable representation for the task. Our code is available at https://github.com/amaloraini/cross-lingual-Z

Specification of the underspecified: A pragmatic analysis of the injunctive in the Rgveda

This paper uses formal pragmatics to show that discourse context alone is inadequate to explain the function of “injunctive” verb forms (i.e., finite verbs unspecified for tense or mood) in Rgvedic Sanskrit. Prior treatments, which explain the temporal and modal specification of the injunctive as being picked up from other verb forms in the immediate discourse, do not fully account for the injunctive’s observed meanings. By applying a framework known in neo-Gricean pragmatics as a “Horn strategy” to tense and modality, I explain the various functions of the injunctive as arising from partial blocking relationships that hold between it and other verb forms with which it competes

Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor the sheer push of the law of numbers a language such as Chinese has. A number of research groups from Sri Lanka have noticed this dearth and the resultant dire need for proper tools and research for Sinhala natural language processing. However, due to various reasons, these attempts seem to lack coordination and awareness of each other. The objective of this paper is to fill that gap of a comprehensive literature survey of the publicly available Sinhala natural language tools and research so that the researchers working in this field can better utilize contributions of their peers. As such, we shall be uploading this paper to arXiv and perpetually update it periodically to reflect the advances made in the field

Coreference Resolution for Arabic

Recently, there has been enormous progress in coreference resolution. These recent developments were applied to Chinese, English and other languages, with outstanding results. However, languages with a rich morphology or fewer resources, such as Arabic, have not received as much attention. In fact, when this PhD work started there was no neural coreference resolver for Arabic, and we were not aware of any learning-based coreference resolver for Arabic since [Björkelund and Kuhn, 2014]. In addition, as far as we know, whereas lots of attention had been devoted to the phemomenon of zero anaphora in languages such as Chinese or Japanese, no neural model for Arabic zero-pronoun anaphora had been developed. In this thesis, we report on a series of experiments on Arabic coreference resolution in general and on zero anaphora in particular. We propose a new neural coreference resolver for Arabic, and we present a series of models for identifying and resolving Arabic zero pronouns. Our approach for zero-pronoun identification and resolution is applicable to other languages, and was also evaluated on Chinese, with results surpassing the state of the art at the time. This research also involved producing revised versions of standard datasets for Arabic coreference

Linear mappings: semantic transfer from transformer models for cognate detection and coreference resolution

Includes bibliographical references.2022 Fall.Embeddings or vector representations of language and their properties are useful for understanding how Natural Language Processing technology works. The usefulness of embeddings, however, depends on how contextualized or information-rich such embeddings are. In this work, I apply a novel affine (linear) mapping technique first established in the field of computer vision to embeddings generated from large Transformer-based language models. In particular, I study its use in two challenging linguistic tasks: cross-lingual cognate detection and cross-document coreference resolution. Cognate detection for two Low-Resource Languages (LRL), Assamese and Bengali, is framed as a binary classification problem using semantic (embedding-based), articulatory, and phonetic features. Linear maps for this task are extrinsically evaluated on the extent of transfer of semantic information between monolingual as well as multi-lingual models including those specialized for low-resourced Indian languages. For cross-document coreference resolution, whole-document contextual representations are generated for event and entity mentions from cross- document language models like CDLM and other BERT-variants and then linearly mapped to form coreferring clusters based on their cosine similarities. I evaluate my results on gold output based on established coreference metrics like BCUB and MUC. My findings reveal that linearly transforming vectors from one model's embedding space to another carries certain semantic information with high fidelity thereby revealing the existence of a canonical embedding space and its geometric properties for language models. Interestingly, even for a much more challenging task like coreference resolution, linear maps are able to transfer semantic information between "lighter" models or less contextual models and "larger" models with near-equivalent performance or even improved results in some cases

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network