Well-Defined Morphology is Sentence-Level Morphology

Morphological tasks have gained decent popularity within the NLP community in the recent years, with large multi-lingual datasets providing morphological analysis of words, either in or out of context. However, the lack of a clear linguistic definition for words destines the annotative work to be incomplete and mired in inconsistencies, especially cross-linguistically. In this work we expand morphological inflection of words to inflection of sentences to provide true universality disconnected from orthographic traditions of white-space usage. To allow annotation for sentence-inflection we define a morphological annotation scheme by a fixed set of inflectional features. We present a small cross-linguistic dataset including semi-manually generated simple sentences in 4 typologically diverse languages annotated according to our suggested scheme, and show that the task of reinflection gets substantially more difficult but that the change of scope from words to well-defined sentences allows interface with contextualized language models.

How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?

Data-driven subword segmentation has become the default strategy for open-vocabulary machine translation and other NLP tasks, but may not be sufficiently generic for optimal learning of non-concatenative morphology. We design a test suite to evaluate segmentation strategies on different types of morphological phenomena in a controlled, semi-synthetic setting. In our experiments, we compare how well machine translation models trained on subword- and character-level can translate these morphological phenomena. We find that learning to analyse and generate morphologically complex surface representations is still challenging, especially for non-concatenative morphological phenomena like reduplication or vowel harmony and for rare word stems. Based on our results, we recommend that novel text representation strategies be tested on a range of typologically diverse languages to minimise the risk of adopting a strategy that inadvertently disadvantages certain languages.

Improved pronunciation prediction accuracy using morphology

Pronunciation lexicons and prediction models are a key component in several speech synthesis and recognition systems. We know that morphologically related words typically follow a fixed pattern of pronunciation which can be described by language-specific paradigms. In this work we explore how deep recurrent neural networks can be used to automatically learn and exploit this pattern to improve the pronunciation prediction quality of words related by morphological inflection. We propose two novel approaches for supplying morphological information, using the word's morphological class and its lemma, which are typically annotated in standard lexicons. We report improvements across a number of European languages with varying degrees of phonological and morphological complexity, and two language families, with greater improvements for languages where the pronunciation prediction task is inherently more challenging. We also observe that combining bidirectional LSTM networks with attention mechanisms is an effective neural approach for the computational problem considered, across languages. Our approach seems particularly beneficial in the low resource setting, both by itself and in conjunction with transfer learning.

Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered

We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially large amount of training data from FSTs for 22 languages, out of which 17 are endangered. The neural models follow the same tagset as the FSTs in order to make it possible to use them as fallback systems together with the FSTs. The source code, models and datasets have been released on Zenodo.

CoDeRooMor: A new dataset for non-inflectional morphology studies of Swedish

The paper introduces a new resource, CoDeRooMor, for studying the morphology of modern Swedish word formation. The approximately 16.000 lexical items in the resource have been manually segmented into word-formation morphemes, and labeled for their categories, such as prefixes, suffixes, roots, etc. Word-formation mechanisms, such as derivation and compounding have been associated with each item on the list. The article describes the selection of items for manual annotation and the principles of annotation, reports on the reliability of the manual annotation, and presents tools, resources and some first statistics. Given the''gold'' nature of the resource, it is possible to use it for empirical studies as well as to develop linguistically-aware algorithms for morpheme segmentation and labeling (cf statistical subword approach). The resource will be made freely available.

Enriching plWordNet with morphology

In the paper, we present the process of adding morphological information to the Polish WordNet (plWordNet). We describe the reasons for this connection and the intuitions behind it. We also draw attention to the specificity of the Polish morphology. We show in which tasks the morphological information is important and how the methods can be developed by extending them to include combined morphological information based on WordNet.

Arabic words Ira'b using expert system

An expert system was developed to consider words' grammar case in Arabic phrases without diacritics. First, the system gets words' morphology and tags using Microsoft tool (ATK), then it depends on Arabic grammar to get words' grammar case in nominal phrases. The system gave a very good results as they compared with Arabic language expert.

Relationship between craniofacial morphology and cervical vertebral posture in patients with skeletal Class II malocclusion

The aim of this study was to investigate the relationship between cervical vertebral posture in patients with skeletal Class II malocclusion by using lateral cephalometric radiographs were taken by natural head position.

first record for Carpophilus hemipterus L. and preliminary evaluation of its infestation rate on fig fruits in Sheen ( Homs-Syria)

In this study Carpophilus hemipterus L. was recorded for the first time on fig fruits in Sheen ( Homs- Syria).Morphological study showed that adult (female and male) have unoval shape measure 3- 4mm,with marked dark brown color and have two yellow spots on its wings and their antennae are clopped.

Photovoltaic Energy Potential Assessment within the Urban Morphology of Damascus

The research problem is neglecting urban policies and construction systems to use renewable energy within urban fabric, in particular solar energy. And it aims to study the relationship between urban morphology and solar energy potential and its role in the establishment of more suitable cities in terms of energy, and thus guide the planning policies to increase utilization of solar energy within cities.