For the analysis of political discourse a reliable identification of group references, i.e., linguistic components that refer to individuals or groups of people, is useful. However, the task of automatically recognizing group references has not yet gained much attention within NLP. To address this gap, we introduce GRIT (Group Reference for Italian), a large-scale, multi-domain manually annotated dataset for group reference recognition in Italian. GRIT represents a new resource for automatic and generalizable recognition of group references. With this dataset, we aim to establish group reference recognition as a valid classification task, which extends the domain of Named Entity Recognition by expanding its focus to literal and figurative mentions of social groups. We verify the potential of achieving automated group reference recognition for Italian through an experiment employing a fine-tuned BERT model. Our experimental results substantiate the validity of the task, implying a huge potential for applying automated systems to multiple fields of analysis, such as political text or social media analysis.

This study explores the prosody of alternative questions (AltQs) in Urdu. We found that AltQs are characterized by a hat pattern that is linked to double contrast sentences, featuring two contrastive foci, similar to German. However, unlike German, the selection of the phrasal accent (PA) is fixed. In Urdu the first PA features an L*(+H)Ha while the second accent has an L*(+H). The hat pattern's full realization depends on the spacing between PAs: greater spacing leads to a prolonged high peak, while reduced spacing results in a compressed hat pattern. Moreover, both the accentual phrase (AP) and the hat pattern carry an L*H contour, with differences in H tone realization: with the hat pattern, the H is significantly higher than that of the AP. We explain the presence of this shared phonological L*H contour by noting that the basic intonational phonology of Urdu is defined by a series of L*H contours, that can then be realized in different ways to show, for example, focus or AltQs.

We present VisInReport, a visual analytics tool that supports the manual analysis of discourse transcripts and generates reports based on user interaction. As an integral part of scholarly work in the social sciences and humanities, discourse analysis involves an aggregation of characteristics identified in the text, which, in turn, involves a prior identification of regions of particular interest. Manual data evaluation requires extensive effort, which can be a barrier to effective analysis. Our system addresses this challenge by augmenting the users' analysis with a set of automatically generated visualization layers. These layers enable the detection and exploration of relevant parts of the discussion supporting several tasks, such as topic modeling or question categorization. The system summarizes the extracted events visually and verbally, generating a content-rich insight into the data and the analysis process. During each analysis session, VisInReport builds a shareable report containing a curated selection of interactions and annotations generated by the analyst. We evaluate our approach on real-world datasets through a qualitative study with domain experts from political science, computer science, and linguistics. The results highlight the benefit of integrating the analysis and reporting processes through a visual analytics system, which supports the communication of results among collaborating researchers.

Abstract This paper presents an open source and extendable Morphological Analyser cum Generator (MAG) for Tamil named Thamizhi Morph. Tamil is a low-resource language in terms of NLP processing tools and applications. In addition, most of the available tools are neither open nor extendable. A morphological analyser is a key resource for the storage and retrieval of morphophonological and morphosyntactic information, especially for morphologically rich languages, and is also useful for developing applications within Machine Translation. This paper describes how Thamizhi Morph is designed using a Finite-State Transducer (FST) and implemented using Foma. We discuss our design decisions based on the peculiarities of Tamil and its nominal and verbal paradigms. We specify a high-level meta-language to efficiently characterise the language’s inflectional morphology. We evaluate Thamizhi Morph using text from a Tamil textbook and the Tamil Universal Dependency treebank version 2.5. The evaluation and error analysis attest a very high performance level, with the identified errors being mostly due to out-of-vocabulary items, which are easily fixable. In order to foster further development, we have made our scripts, the FST models, lexicons, Meta-Morphological rules, lists of generated verbs and nouns, and test data sets freely available for others to use and extend upon.

This study looks at the role of lexical stress in Urdu prosody. The literature on lexical stress is divided, with some authors developing algorithms for stress assignment, while others deny its relevance for prosody. We performed three experiments to investigate this issue. We found evidence that a strong increase in the duration of a syllable indicates stress and that lexical stress and phrasal intonation interact in a non-trivial manner. We also found that stress perception varies according to syllable weight with weight clash being a determining factor.

This paper adds to the available resources for the under-resourced language Urdu by converting different types of existing treebanks for Urdu into a common format that is based on Universal Dependencies. We present comparative results for training two dependency parsers, the MaltParser and a transition-based BiLSTM parser on this new resource. The BiLSTM parser incorporates word embeddings which improve the parsing results significantly. The BiLSTM parser outperforms the MaltParser with a UAS of 89.6 and an LAS of 84.2 with respect to our standardized treebank resource.

Jabeen F, Bögel T, Butt M. Immediately postverbal questions in Urdu. Presented at the InterSpeech, Dresden, Germany.