ymoslem/acl-abstracts huggingface dataset:
Corpus--based Research into Derivational Morphology: A Comparative Study of Japanese and English Verbalization (2024)

As part of elucidating the syntax-morphology interaction, this study investigates where and how complex verbs are formed in Japanese and English. Focusing on the Japanese verb-forming suffix -ka-suru (e.g. toshi-o gendai-ka-suru {\textquoteleft}modernize city'), relevant verbs are extracted from a large-scale corpus and they receive an in-depth analysis from semantic, morphosyntactic, and functional viewpoints. The properties of -ka-suru and those of its English counterpart are then compared and contrasted. The result reveals three main points: (i) -ka-suru verbs are constantly created in syntactic settings to fulfill the functions of brevity and conceptualization, (ii) while denominal -ize derivatives have several submeanings such as {\textquoteleft}result,' {\textquoteleft}ornative,' and {\textquoteleft}agentive,' -ka-suru equivalents retain the meaning {\textquoteleft}result,' and (iii) -ka-suru can be combined with compound nouns, but -ize cannot. We will demonstrate that the above features originate in the underlying syntactic structure related to each suffix and their difference, thus supporting the thesis of syntactic word formation. (1) ji-kokumin-o moomai-ka-suru one`s-people-ACC ignorant-change-do {\textquoteleft}make one`s people ignorant' (2) shinikaketa momiji-o bonsai-ka-suru dying maple-ACC bonsai-change-do {\textquoteleft}turn a dying maple into a bonsai'
The Verbal Category of Conditionality in Bulgarian and its Ukrainian Correspondences (2024)

Modern Bulgarian shares a conditional mood with the other Slavic languages, but it also has developed a future-in-the-past tense which is structurally analogous to many Western European languages' category traditionally called a conditional mood in their grammars. The distinction between these two forms is sometimes elusive and can be difficult for native speakers of Slavic languages who are learning Bulgarian. In this paper we consider the uses of the Bulgarian conditional mood and future-in-the-past tense in a parallel corpus of Bulgarian and Ukrainian text, examining the corresponding wording in Ukrainian, where the conditional mood is supplemented by modal verbs, and discuss the breadth of choices open to translators when working in each direction.
Lexical Richness of French and Quebec Journalistic Texts (2024)

This paper presents some results of a quantitative study that focuses on the variety and word frequency in texts from a comparative perspective. The study aims to analyze and compare French and Quebec journalistic texts on political and cultural topics written in French and recently published in major newspapers such as Le Monde, le Figaro, Le Devoir, etc. The statistical analysis concerns the number of different words in the text, the number of different adjectives, the number of different verbs (and also passive structures, participles and gerunds which contribute to syntactic and stylistic sophistication), and the number of hapaxes. French texts from France exhibit greater lexical richness and sophistication: they contain more adjectives, a greater variety of adjectives, as well as more participles and gerunds compared to French texts from Quebec. The originality of the study lies in the fact that it analyzes variation in French using a lexicometric approach.
A Corpus of Liturgical Texts in German: Towards Multilevel Text Annotation (2024)

The aim of the study is to create a {\textquotedblleft}documented{\textquotedblright} literary and theological history of German Catholic hymnography. The paper focuses on the creation of a corpus of liturgical texts in German and describes the first stage of annotation dealing with the metatextual markup of Catholic hymns. The authors dwell in detail on the parameters of the multi-level classification of hymn texts they developed, which allows them to differentiate hymns on different grounds. The parameters include not only characteristics that represent hymns (the period and the source of their origin, rubrics, musical accompaniment), but also ones that are inherent for strophes. Based on the created markup, it is possible to trace general trends in texts divided according to certain meta-features. The developed scheme of annotation is given on the example of the hymnbook Gotteslob (1975). The results present statistics on different parameters used for hymn description.
EurLexSummarization -- A New Text Summarization Dataset on EU Legislation in 24 Languages with GPT Evaluation (2024)

Legal documents are notorious for their length and complexity, making it challenging to extract crucial information efficiently. In this paper, we introduce a new dataset for legal text summarization, covering 24 languages. We not only present and analyze the dataset but also conduct experiments using various extractive techniques. We provide a comparison between these techniques and summaries generated by the state-of-the-art GPT models. The abstractive GPT approach outperforms the extractive TextRank approach in 8 languages, but produces slightly lower results in the remaining 16 languages. This research aims to advance the field of legal document summarization by addressing the need for accessible and comprehensive information retrieval from lengthy legal texts.
On a Hurtlex Resource for Bulgarian (2024)

The paper reports on the cleaning of the Hurtlex lexicon for Bulgarian as part of the multilingual Hurtlex resource. All the challenges during the cleaning process are presented, such as: deleting strings or lexica that are clear errors from the automatic translation, establishing criteria for keeping or discarding a lexeme based on its meaning and potential usages, contextualizing the lexeme with the meaning through an example, etc. In addition, the paper discusses the mapping of the offensive lexica to the BTB-Wordnet as well as the system that has been used.
Unified Annotation of the Stages of the Bulgarian Language. First Steps (2024)

The paper reports on an ongoing work on a proposal of guidelines for unified annotation of the stages in the development of the Bulgarian language from the Middle Ages to the early modern period. It discusses the criteria for the selection of texts and their representation, along with some results of the trial tagging with an existing tagger which was already trained on other texts.
ChatGPT: Detection of Spanish Terms Based on False Friends (2024)

One of the common errors which translators commit when transferring terms from one lan- guage into another is erroneously coining terms which are based on a false friend mistake due to the similarity between lexical units forming part of terms. In this case-study, we use Chat- GPT to automatically detect terms in Spanish which may be coined based on a false friend relation. To carry out this study, we imple- mented two experiments with GPT and com- pared the results. In the first, we prompted GPT to produce a list of twenty terms in Span- ish extracted from the UN discourse, which are possibly based on false friend relation, and its English equivalents and analysed the veracity of the results. In the second experiment, we used an aligned corpus to further study the ca- pabilities of the Language Model on detecting false friends in English and Spanish Text. Some results were significant for future terminologi- cal studies.
Deep Learning Framework for Identifying Future Market Opportunities from Textual User Reviews (2024)

The paper develops an application of design gap theory for identification of future market segment growth and capitalization from a set of customer reviews for bought products from the market in a given past period. To build a consumer feature space, an encoded-decoder network with attention is trained over the textual reviews after they are pre-processed through tokenization and embedding layers. The encodings for product reviews are used to train a variational auto encoder network for representation of a product feature space. The sampling capabilities of this network are extended with a function to look for innovative designs with high consumer preferences, characterizing future opportunities in a given market segment. The framework is demonstrated for processing of Amazon reviews in consumer electronics segment.
Look Who`s Talking: The Most Frequently Used Words in the Bulgarian Parliament 1990-2024 (2024)

In this study we identify the most frequently used words and some multi-word expressions in the Bulgarian Parliament. We do this by using the transcripts of all plenary sessions between 1990 and 2024 - 3,936 in total. This allows us both to study an interesting period known in the Bulgarian linguistic space as the years of {\textquotedblleft}transition and democracy{\textquotedblright}, and to provide scholars of Bulgarian politics with a purposefully generated list of additional stop words that they can use for future analysis. Because our list of words was generated from the data, there is no preconceived theory, and because we include all interactions during all sessions, our analysis goes beyond traditional party lines. We provide details of how we selected, retrieved, and cleaned our data, and discuss our findings.
Estimating Commonsense Knowledge from a Linguistic Analysis on Information Distribution (2024)

Commonsense Knowledge (CSK) is defined as a complex and multifaceted structure, encompassing a wide range of knowledge and reasoning generally acquired through everyday experiences. As CSK is often implicit in communication, it poses a challenge for AI systems to simulate human-like interaction. This work aims to deepen the CSK information structure from a linguistic perspective, starting from its organisation in conversations. To achieve this goal, we developed a three-level analysis model to extract more insights about this knowledge, focusing our attention on the second level. In particular, we aimed to extract the distribution of explicit actions and their execution order in the communicative flow. We built an annotation scheme based on FrameNet and applied it to a dialogical corpus on the culinary domain. Preliminary results indicate that certain frames occur earlier in the dialogues, while others occur towards the process`s end. These findings contribute to the systematic nature of actions by establishing clear patterns and relationships between frames.
Pondera: A Personalized AI--Driven Weight Loss Mobile Companion with Multidimensional Goal Fulfillment Analytics (2024)

The global obesity epidemic is a significant challenge to public health, necessitating innovative and personalized solutions. This paper presents Pondera, an innovative mobile app revolutionizing weight management by integrating Artificial Intelligence (AI) and multidimensional goal fulfilment analytics. Pondera distinguishes itself by supplying a tailored approach to weight loss, combining individual user data, including dietary preferences, fitness levels, and specific weight loss objectives, with advanced AI algorithms to generate personalized weight loss plans. Future development directions include refining AI algorithms, enhancing user experience, and validating effectiveness through comprehensive studies, ensuring Pondera becomes a pivotal tool in achieving sustainable weight loss and health improvement.
Mitigating Hallucinations in Large Language Models via Semantic Enrichment of Prompts: Insights from BioBERT and Ontological Integration (2024)

The advent of Large Language Models (LLMs) has been transformative for natural language processing, yet their tendency to produce {\textquotedblleft}hallucinations{\textquotedblright}{---}outputs that are factually incorrect or entirely fabricated{---} remains a significant hurdle. This paper introduces a proactive methodology for reducing hallucinations by strategically enriching LLM prompts. This involves identifying key entities and contextual cues from varied domains and integrating this information into the LLM prompts to guide the model towards more accurate and relevant responses. Leveraging examples from BioBERT for biomedical entity recognition and ChEBI for chemical ontology, we illustrate a broader approach that encompasses semantic prompt enrichment as a versatile tool for enhancing LLM output accuracy. By examining the potential of semantic and ontological enrichment in diverse contexts, we aim to present a scalable strategy for improving the reliability of AI-generated content, thereby contributing to the ongoing efforts to refine LLMs for a wide range of applications.
Commercially Minor Languages and Localization (2024)

This paper offers a perspective of languages with a less significant volume of digital usership as minor in the context of globalization and localization. With this premise, the risks this status poses to the quality of localized texts, the substantiality of genre conventions, the public image of professional translators, and the users' linguistic competence in these languages is explored. Furthermore, the common lack of established or clear conventions in the localization of digital products into commercially minor languages (and in the digital product genres) is highlighted as one of the factors amplifying these risks. These perspectives are contextualized with the Bulgarian language with examples of errors encountered in Bulgarian digital content localized from English and more specifically {--} errors and problems related to gender neutrality and register.
Semantic features in the automatic analysis of verbs of creation in Bulgarian and English (2024)

The paper focuses on the semantic class of verbs of creation as a subclass of dynamic verbs. The objective is to present the description of creation verbs in terms of their corresponding semantic frames and to outline the semantic features of the frame elements with a view to their automatic identification and analysis in text. The observations are performed on Bulgarian and English data with the aim to establish the language-independent and language-specific features in the semantic description of the analysed class of verbs.
A \textquoteleftDipdive' into Motion: Exploring Lexical Resources towards a Comprehensive Semantic and Syntactic Description (2024)

In this paper I illustrate the semantic description of verbs provided in three semantic resources (FrameNet, VerbNet and VerbAtlas) in comparative terms with a view to identifying common and distinct components in their representation and obtaining a preliminary idea of the resources' interoperability. To this end, I provide a comparison of a small sample of motion verbs aligned with semantic frames and classes in the three resources. I also describe the semantic annotation of Bulgarian motion verbs using the framework defined in the Berkeley FrameNet project and its enrichment with information from the other two resources, which has been enabled by the mapping between: (i) their major semantic units {--} FrameNet frames, VerbNet classes and VerbAtlas frames, and (ii) their `building blocks' {--} frame elements (FrameNet )and semantic roles (VerbNet, VerbAtlas).
Multilingual Corpus of Illustrative Examples on Activity Predicates (2024)

The paper presents the ongoing process of compilation of a multilingual corpus of illustrative examples to supplement our work on the syntactic and semantic analysis of predicates representing activities in Bulgarian and other languages. The corpus aims to include over 1,000 illustrative examples on verbs from six semantic classes of predicates (verbs of motion, contact, consumption, creation, competition and bodily functions) which provide a basis for observations on the specificity of their realisation. The corpus of illustrative examples will be used for contrastive studies and further elaboration on the scope and behaviour of activity verbs in general, as well as its semantic subclasses.
Large Language Models in Linguistic Research: the Pilot and the Copilot (2024)

In this paper, we present two experiments focussing on linguistic classification and annotation of examples, using zero-shot prompting. The aim is to show how large language models can confirm or reject the linguistic judgements of experts in order to increase the productivity of their work. In the first experiment, new lexical units evoking a particular FrameNet semantic frame are selected simultaneously with the annotation of examples with the core frame elements. The second experiment attempts to categorise verbs into the aspectual classes, assuming that only certain combinations of verbs belonging to different aspectual classes evoke a semantic frame. The linguistic theories underlying the two experiments, the development of the prompts and the results of the experiments are presented.
Critical Size Hypothesis: How Model Hyperparameters Correlate with Its Linguistic Abilities (2024)

In recent years, the models were tested on different probing tasks to examine their language knowledge. However, few researchers explored the very process of models' language acquisition. Nevertheless, the analysis of language acquisition during training could shed light on the model parameters that help to acquire the language faster. In this work, we experiment with model hyperparameters and reveal that the hidden size is the most essential factor for model language acquisition.
INIKOL - Collocational Database for Learning Croatian as a Foreign Language (2024)

This paper describes the ongoing work on the INIKOL project - the development of a collocation database for learning Croatian as a foreign language. The main goal of the project is to contribute to easier mastery of collocations as fixed phrases in Croatian as a foreign language.
How Does an Adjective Sound Like? Exploring Audio Phrase Composition with Textual Embeddings (2024)

We learn matrix representations for the fre- quent sound-relevant adjectives of English and compose them with vector representations of their nouns. The matrices are learnt jointly from audio and textual data, via linear regres- sion and tensor skipgram. They are assessed using an adjective similarity benchmark and also a novel adjective-noun phrase similarity dataset, applied to two tasks: semantic similar- ity and audio similarity. Joint learning via Ten- sor Skipgram (TSG) outperforms audio-only models, matrix composition outperforms addi- tion and non compositional phrase vectors.
Learning through gesture: embodied repetitions in tandem interactions (2024)

Grounded in an interactional approach, this corpus-based study presents an analysis of multimodal tandem interactions held in English between tandem partners (L1 and L2 speakers) to study other-repetitions across different levels and modalities. In particular, I investigate cases of embodied repetitions in contexts of co-construction and repair whereby tandem partners negotiate meaning. Based on careful micro-analyses of data fragments, analyses reveal different types of temporal coordination between the repetition of the target item and/or of the gesture, addressing specific issues at different linguistic levels. While repetitions typically occur in linguistic-oriented contexts, emerging gestures may further contribute to mutual understanding and alignment.
Towards Automated Game-Based Early Screening for Language Disorder (2024)

This paper examines the potential of gamifying early childhood language disorder screening to make the process more accessible and scalable. We provide an overview of current practices in screening and assessment, and a description of our on-going work towards automation of early screening. By integrating developmental milestones into a video game format and employing automatic speech recognition and natural language processing, this approach aims to enhance the efficiency and reach of early screening in order to identify children who need further professional assessment.
L2 Interaction in Heterogeneous Learner Groups during Content and Language Integrated Learning: The Experience of (removed for peer-review) and beyond (2024)

{\CYRS}ontent and language integrated learning is considered a powerful tool to promote inclusion in educational settings of learners for whom the language of instruction is their additional language. Language-related difficulties of those learners have been claimed detrimental for attaining personal educational goals. Academic language places increased cognitive demands on the learning process in general due to 1) its internal complexity; 2) L2 speakers' lower proficiency; 3) their disadvantage in terms of real-time processing. Facilitators are, therefore, encouraged to integrate interactional CLIL-elements (e.g., scaffolding) during content instruction that provide the necessary pedagogical support for better understanding of disciplinary concepts and their interrelation. In the current contribution, we present the concept and first results of Rail.lexis, a collaborative project of the Department of German Studies and the Department of Railway Engineering at TU Brauschweig. We present and discuss several conversational arrangements (e.g., word guessing games, a differential task matrix) that were designed to engage the learners of heterogeneous linguistic backgrounds in meaningful interactions in subject-specific classes. Subject-specific tasks are gradient regarding their cognitive complexity and the background knowledge required to solve them. Therefore, the linguistic repertoire required to negotiate different task types is also differential to ensure the participation of linguistically diverse students in language-enhanced classroom interactions.
Fifty shapes of BLiMP: syntactic learning curves in language models are not uniform, but sometimes unruly (2024)

Syntactic learning curves in LMs are usually reported as relatively stable and power law-shaped. By analyzing the learning curves of different LMs on various syntactic phenomena using both small self-trained llama models and larger pre-trained pythia models, we show that while many phenomena do follow typical power law curves, others exhibit S-shaped, U-shaped, or erratic patterns. Certain syntactic paradigms remain challenging even for large models, resulting in persistent preference for ungrammatical sentences. Most phenomena show similar curves for their paradigms, but the existence of diverging patterns and oscillations indicates that average curves mask important developments, underscoring the need for more detailed analyses of individual learning trajectories.
Not Just Semantics: Word Meaning Negotiation in Social Media and Spoken Interaction (2024)

This paper outlines the ongoing research project {\textquotedblleft}Not Just Semantics: Word Meaning Negotiation in Social Media and Spoken Interaction{\textquotedblright}. The goal of the project is to investigate how meanings of words (and phrases) are interactively negotiated in social media and in spoken interaction. This project will contribute towards a comprehensive theory of word meaning negotiation.
Toward Real Time Word Based Prosody Recognition (2024)

Prosodic salience is a heuristic based on word-level prosody in child-directed speech that is thought to serve as a cue for attentional focus. It has been used in the context of robotic language acquisition to extract the contextually most relevant words from a human tutor`s speech to ground them in a robot`s sensorimotor data. However, the pipeline for performing word-based prosody-recognition operated in a semi-automatic manner and required substantial manual effort. We describe our efforts to automate the existing pipeline by including real time prosody recognition, and a modern speech recognition and forced alignment model. The intention is to enable its use in real time for human-in-the-loop robotic language acquisition and other socially driven forms of online learning.