diff --git "a/topics.json" "b/topics.json"
new file mode 100644--- /dev/null
+++ "b/topics.json"
@@ -0,0 +1,57720 @@
+{
+  "topic_representations": {
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+          "Pre-trained Language Models and Embeddings for Multilingual Natural Language Processing\n\nThis topic focuses on the use of pre-trained language models and embeddings in natural language processing (NLP), specifically in multilingual settings. These techniques have recently shown remarkable success in improving the performance of various downstream NLP tasks. However, pre-training deep language models over large-scale corpora is a resource-intensive process. Self-supervised pre-training of transformer models has shown some promise in reducing the need for task-specific labelled data for fine-tuning. In addition, recent advancements in model size, dataset size, and computation power have allowed for the training of large models like GPT-3 that demonstrate excellent performance in zero-shot and few-shot learning scenarios. This topic also touches on the use of neural semantic parsing approaches for question answering systems over knowledge graphs. Finally, the topic explores how social media platforms like Twitter can be used to inform rescue agencies during emergencies.",
+          1
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+      ],
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+          1
+        ]
+      ],
+      "1": [
+        [
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+          1
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+      ],
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+        [
+          "Pre-trained language models and parameter-efficient methods for language modeling, including prompt tuning and few-shot learning for NLP tasks. This topic explores the benefits and challenges of using pre-trained language models, such as BERT, and various approaches to adapting them to downstream tasks, including prompt tuning for tuning only soft prompts and few-shot learning for parameter-efficient fine-tuning. These methods aim to improve the efficiency of executing pre-trained language models while maintaining or improving their performance on various NLP tasks.",
+          1
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+          "Text Summarization Models and Systems\n\nThe topic of text summarization models and systems is centered around the use of automated methods to generate brief yet informative summaries of large amounts of textual data. This is becoming increasingly important as the amount of text data available online continues to rapidly increase. There are two main approaches to text summarization: extractive and abstractive. Extractive summarization involves selecting important sentences or phrases from a source document and presenting them as a summary, while abstractive summarization aims to rephrase and condense the information into a new summary. However, both approaches still face challenges in terms of accuracy, interpretability, and rationale. Evaluation of text summarization systems is typically based on metrics that compare the system-generated summary with a set of human-written gold-standard summaries. There is still much to explore in the field of text summarization, especially for languages other than English and for multi-document summarization.",
+          1
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+      "4": [
+        [
+          "Machine Reading Comprehension and Question Answering in the QA Domain\n\nThis topic focuses on the development and use of machine reading comprehension (MRC) and question answering (QA) techniques in the QA domain. It involves constructing massive QA pairs from various documents and datasets, such as open-domain sources and multi-subject data. The ultimate goal is to enable models to effectively understand questions, collect relevant information, and reason over evidence to provide accurate answers. Despite much recent research in this area, there is still much room for improvement, and it remains a challenging task.",
+          1
+        ]
+      ],
+      "5": [
+        [
+          "Sentiment Analysis and Aspect-based Sentiment Analysis (ABSA)\n\nThis topic revolves around sentiment analysis, which involves identifying and extracting sentiment information from various forms of textual data such as online reviews, user comments, and social media posts. One of the subfields within sentiment analysis is aspect-based sentiment analysis (ABSA), which focuses on identifying the sentiment polarity of specific aspects or entities within the text. \n\nThe documents within this topic discuss various aspects of sentiment analysis and ABSA, including aspect-level sentiment classification (ALSC), sentiment polarity classification challenges, aspect sentiment triplet extraction (ASTE), and summarizing previous approaches to ABSA. Overall, this topic highlights the importance of sentiment analysis in various domains, including NLP and product reviews, and the need for more nuanced approaches such as ABSA to provide more detailed information about sentiment polarity.",
+          1
+        ]
+      ],
+      "6": [
+        [
+          "This topic revolves around the field of natural language processing (NLP) and its application in electronic health records (EHRs). The focus is on tasks such as named entity recognition, entity recognition, and relation extraction from clinical text. In particular, the topic highlights the challenges of extracting valuable information from unstructured clinical notes that contain social and behavioral determinants of health (SBDoH) and social determinants of health (SDOH) which are not coded in structured forms in EHRs. The proposed solution is to use NLP techniques to automate annotation and save significant time and effort spent by human coders. The main purpose of this topic is to improve patient outcomes and healthcare delivery by leveraging NLP to extract valuable insights from clinical text and EHRs.",
+          1
+        ]
+      ],
+      "7": [
+        [
+          "This topic pertains to the field of machine translation, with a focus on neural machine translation models and their performance and quality in various translation tasks. The documents cover topics such as the development of NMT and its comparison to statistical machine translation systems, the use of monolingual data to improve translation quality, back-translation as a method for generating training data, the use of unified attentional encoder-decoder neural networks for translation, and investigating the potential of attention-based NMT for simultaneous translation.",
+          1
+        ]
+      ],
+      "8": [
+        [
+          "Natural Language Generation and Text Generation Tasks\n\nThis topic focuses on the development and improvement of natural language generation (NLG) and text generation techniques, with a particular emphasis on sequence-to-sequence deep learning models such as Transformer-based language models and pre-trained models like BART and T5. The topic highlights the challenges of generating coherent and semantically meaningful text for various applications, including machine translation, dialogue systems, and image captioning. Additionally, the topic explores new solutions to overcome issues such as exposure bias in generation tasks, including the use of contrastive learning and policy gradient techniques. The role of Generative Adversarial Nets (GANs) in improving generation performance is also discussed, particularly in the context of language modelling and word embeddings such as GLoVe and word2vec.",
+          1
+        ]
+      ],
+      "9": [
+        [
+          "Hate speech detection and bias in offensive language using deep learning and LSTM speech detection. This topic explores the relevance and need for detecting hate speech on social media platforms and the challenges faced in developing accurate models. It also focuses on understanding the biases and interpretability aspects of hate speech and the resources required for effective detection. The use of deep learning and LSTM speech detection techniques are highlighted as potential methods for improving hate speech detection.",
+          1
+        ]
+      ],
+      "10": [
+        [
+          "The detection and prevention of fake news and disinformation through credibility analysis, fact-checking, and timely intervention on social media, with a focus on how fake news propagates through networks and the challenges of identifying and evaluating sources of misinformation.",
+          1
+        ]
+      ],
+      "11": [
+        [
+          "This topic covers the field of entity relation extraction, which involves identifying and categorizing relationships between entities within unstructured text. It specifically focuses on techniques for relation extraction and classification, open information extraction, and knowledge base modeling, as well as the use of entity pairs to improve information retrieval systems. The topic also addresses challenges in relation extraction from spoken language and the need for explainability in such methods.",
+          1
+        ]
+      ],
+      "12": [
+        [
+          "Named Entity Recognition (NER) in Natural Language Processing (NLP) is the task of identifying and categorizing spans of text that represent entities, such as people, organizations, locations, and dates. This task is important in many NLP applications, including machine translation, information retrieval, and question answering. NER can be categorized into flat, nested, and discontinuous subtasks depending on the complexity of the entity spans. NER models can greatly benefit from the use of external knowledge sources. However, for extreme low-resource languages with limited tagged data samples, developing accurate NER systems can be challenging.",
+          1
+        ]
+      ],
+      "13": [
+        [
+          "This topic concerns the various techniques and models used for parsing natural language text into structured representations, including dependency parsing, constituency parsing, and other parsing models. The specific focus is on the differences between transition-based and graph-based models, as well as the use of machine learning to predict parsing actions. Additionally, the topic includes a novel technique for reducing phrase-representation parsing to dependency parsing using head-ordered dependency trees. Several papers are discussed that explore these topics and propose state-of-the-art methods for dependency parsing in English and Chinese.",
+          1
+        ]
+      ],
+      "14": [
+        [
+          "This topic pertains to event extraction, detection, and information extraction, which are essential components of information extraction tasks aimed at understanding the world. The focus is on the detection and extraction of event triggers and corresponding event arguments, as well as event types and their relations. The subtasks involved in event extraction are often decomposed and integrated into various pipelines or models using state-of-the-art components. The topic covers approaches that address challenging tasks such as document-level event extraction and the extraction of event and entity coreference. Furthermore, the discussion involves exploring new ways of modeling event extraction that rely less on annotated event mentions.",
+          1
+        ]
+      ],
+      "15": [
+        [
+          "Emotion analysis and recognition in natural language processing, with a focus on exploring the causes and context of emotions, as well as creating human-like conversational systems that can emotionally connect with users. This involves research on emotion recognition, detection, classification, sentiment analysis, emotional valence, and arousal, with an emphasis on tackling emotions as a phenomenon through structured learning. There is also an interest in discovering emotions and their causes in conversations and exploring the potential of putting emotions into event context.",
+          1
+        ]
+      ],
+      "16": [
+        [
+          "Word embeddings and their use in natural language processing. The documents in this subset discuss the benefits and limitations of using distributed word embeddings, such as those learned by methods like word2vec, for various NLP tasks. The documents also explore techniques for combining multiple sources of embeddings and using self-supervised learning to improve embedding quality. Finally, the documents highlight the importance of named entities in many NLP tasks and the challenge of incorporating them into word representations.",
+          1
+        ]
+      ],
+      "17": [
+        [
+          "Natural language explanations and their generation for NLP models, including the use of human explanations, based explanations, and attention as a mechanism for interpreting the importance of input tokens for predictions. The topic also covers the potential benefits and challenges of generating natural language explanations for NLP models, such as providing additional information and supervision for prediction, while requiring a large amount of data and transparency for building trust with stakeholders.",
+          1
+        ]
+      ],
+      "18": [
+        [
+          "Morphological Analysis in Morphologically Rich Languages \n\nThis topic focuses on the study of morphological analysis and processing in languages with complex morphological structures, commonly known as morphologically rich languages. It includes a discussion on different approaches and techniques for morphological analysis and disambiguation, as well as developments in natural language processing for morphologically rich languages. The documents in the subset cover various languages, such as Arabic, Turkish, Hungarian, Kinyarwanda, and Estonian, and explore different aspects of morphological analysis and processing, including the use of automata, unsupervised learning, and neural network models.",
+          1
+        ]
+      ],
+      "19": [
+        [
+          "This topic is about the analysis of text data for topic discovery using topic modeling methods such as Latent Dirichlet Allocation and neural topic models. The focus is on the limitations of traditional topic models, such as the inability to model word order and lack of interpretability, as well as newer approaches to improving these methods. The goal is to extract meaningful patterns and labels of topics from text corpora, including dynamic topic modeling for time-varying patterns and word embedding models for semantic relationships between words and documents.",
+          1
+        ]
+      ],
+      "20": [
+        [
+          "The presence of gender biases in word embeddings and its impact on social biases and stereotypes in natural language processing and machine learning. This includes the recognition of biases in NLP models, the concern over the perpetuation of social biases, and the analysis of gender biases in various word embedding models. Additionally, the topic discusses the limitations and challenges in mitigating bias, as well as the need for studying biases in languages beyond English.",
+          1
+        ]
+      ],
+      "21": [
+        [
+          "This topic explores the relationship between word frequency, word length, and linguistic laws, specifically focusing on Zipf's law. It discusses the various linguistic laws formulated from the research of G.K. Zipf and how the frequency of a word is often found to be a power law function of its frequency rank. Additionally, it examines the potential of Zipf's meaning-frequency law and Zipf's law of abbreviation to become linguistic universals. The topic primarily relates to the field of linguistics and the study of natural languages.",
+          1
+        ]
+      ],
+      "22": [
+        [
+          "The role of natural language processing (NLP) and language models in the legal domain, particularly in the areas of legal document analysis and information retrieval. This includes the use of pre-trained language models (PLMs) such as LegalBERT for processing legal text, the challenges and advancements in legal case retrieval, and the potential benefits of artificial intelligence (AI) in the legal system. Large language models (LLMs) have also had a significant impact in the field of law and have transformed many areas of NLP, including legal opinion text analysis.",
+          1
+        ]
+      ],
+      "23": [
+        [
+          "The vulnerability of natural language processing models to adversarial attacks and efforts to improve their robustness through adversarial training and data augmentation.",
+          1
+        ]
+      ],
+      "24": [
+        [
+          "This topic focuses on commonsense question answering models and knowledge, as well as commonsense reasoning and inference. It explores the use of large-scale language models for pre-training and cross-lingual applications, as well as the challenges of leveraging commonsense knowledge in these models. Additionally, it delves into the construction of automatic knowledge bases for commonsense reasoning and the question of whether the training process leads to the acquisition of knowledge, inference capability, or both.",
+          1
+        ]
+      ],
+      "25": [
+        [
+          "A discussion of the application of compositional distributional semantics in natural language processing, with a focus on its quantum syntactic linguistic and compositional properties. This includes the use of lexical representations to provide semantics for grammatical structures, as well as the combination of statistical vector space and compositional models of grammar. The topic also covers the application of compositional approaches in fields such as physics, cognitive science, and NLP.",
+          1
+        ]
+      ],
+      "26": [
+        [
+          "Machine Translation Error Correction and Spelling/Grammatical Error Detection\n\nThis topic is about the various methods and systems used to correct errors in machine-generated or human-typed text, with a focus on correcting spelling and grammatical errors. It covers a range of techniques including neural machine translation, error detection, and corpus-based approaches. The documents in this topic touch on various aspects of the topic, such as the difficulty of achieving human-level language understanding, the importance of training data, and the challenges of correcting errors in languages other than English.",
+          1
+        ]
+      ],
+      "27": [
+        [
+          "This topic pertains to the analysis and processing of arguments and debates through the use of natural language processing techniques. The focus is on identifying and extracting arguments from large text corpora through either keyword or sentence/document-level search, as well as generating and assessing the quality of arguments for use in debates. Additionally, there is a consideration for understanding the context and stance of claims within these arguments. The goal is to provide insights into argumentation and discourse for various applications.",
+          1
+        ]
+      ],
+      "28": [
+        [
+          "This topic explores the use of sentiment analysis to detect sarcasm and humorous figurative language in textual data. It highlights the challenges of detecting sarcasm in sentiment-bearing text and the distinction between intended and perceived sarcasm. The documents in this topic investigate different approaches to detecting sarcasm and the verbal and non-verbal cues that can be used to express it. The role of social networks in mass-producing new avenues of socialization and communication is also discussed.",
+          1
+        ]
+      ],
+      "29": [
+        [
+          "Coreference resolution is a challenging area of natural language processing that seeks to identify all textual references to the same real-world entity. This includes tasks such as coreference mention detection, pronoun resolution, and resolving referents across corpora using annotated annotations. Recent models rely on span representations to find potential links between word spans and pruning techniques to reduce the number of potential links. Evaluation of these models is often done on multiple datasets which vary in how coreference is realized. While significant progress has been made in recent years for coreference resolution within a single document scope, the more challenging task of cross-document (CD) coreference resolution remains relatively unexplored. Overall, research in this field is divided into coreference resolution and anaphora, with attention focused on improving end-to-end models for success in these tasks.",
+          1
+        ]
+      ],
+      "30": [
+        [
+          "This topic pertains to the various techniques and methods used for word sense disambiguation and sense induction in natural language processing, particularly focused on word embeddings. The documents in this subset cover the use of sense embeddings, frequency-based relationships, exploration of unsupervised word embeddings, the challenge of unevenly distributed word senses, and techniques for identifying the appropriate sense in context. WordNet and word sense induction are also mentioned as relevant concepts.",
+          1
+        ]
+      ],
+      "31": [
+        [
+          "Knowledge Graph Completion and Embedding for Link Prediction in Knowledge Graphs\n\nThis topic is about completing knowledge graphs (KGs) by predicting missing triples and improving link prediction in KGs using embedding techniques. KGs are incomplete as they do not have all the necessary data. Hence, completing KGs is necessary to improve their effectiveness in various natural language processing tasks. Link prediction is an essential task in KGs for predicting missing entities or relations. Existing models for link prediction mainly focus on representing knowledge graph triplets. However, embedding-based methods perform poorly on relations that only have a few associative triples. Therefore, novel embedding models such as NoGE are introduced to improve knowledge graph completion and link prediction by integrating co-occurrence among entities and relations into graph neural networks. The topic also touches upon Multi-Source KGs, which are a common situation in real KG applications.",
+          1
+        ]
+      ],
+      "32": [
+        [
+          "Semantic parsing and its neural and oriented approaches for generating abstract meaning representations from natural language utterances for executing against real-world environments such as databases. Challenges include scaling to arbitrary domains and utilizing contextual information, while recent growth in abstract meaning representation (AMR) parsing has been observed with a focus on improving domain-specific perfo",
+          1
+        ]
+      ],
+      "33": [
+        [
+          "Lingual Transfer Learning and Cross-Lingual Transfer in Multilingual Language Models\n\nThis topic explores the use of lingual transfer learning and cross-lingual transfer in training multilingual language models. The aim is to improve natural language processing systems for low-resource languages by transferring knowledge from high-resource ones. The focus is on pre-trained multilingual language encoders like multilingual BERT, XLM-R, and mT5, which have shown great potential for zero-shot cross-lingual transfer. However, the challenge is to precisely align words and phrases across languages, which impacts the transfer performance on low-resource languages. Researchers have examined the effectiveness of joint reduction of language-specific model parameters and improved training methods for improved zero-shot transfer. The documents in this subset investigate different aspects of lingual transfer learning and cross-lingual transfer, including the benefits for languages with little to no training data, the limitations of pre-trained models, and efforts to improve performance on low-resource languages.",
+          1
+        ]
+      ],
+      "34": [
+        [
+          "Machine Translation Evaluation and Quality Assessment \nThis topic focuses on the evaluation and assessment of machine translation (MT) systems in improving translation quality. It explores various techniques for comparing and evaluating the quality of machine-translated output with human-translated output, and for identifying potential translation errors. It also discusses different approaches to quality estimation (QE) and automatic post-editing (APE) that can help streamline the MT process and improve translation quality. Additionally, the topic highlights the importance of QE in real-world applications of MT, and the challenges of developing QE models that are accurate and effective in different contexts and languages.",
+          1
+        ]
+      ],
+      "35": [
+        [
+          "This topic is about text-to-SQL parsers and their ability to convert natural language questions into structured query language (SQL) statements to query relational databases. The challenge lies in generating complex SQL queries with multiple clauses and sub-queries while generalizing to new, unseen databases. Some methods focus on using history context, previously predicted SQL, or an SQL intermediate representation, such as Natural SQL (NatSQL), to bridge the gap between natural language and SQL. The task has practical applications in simplifying database query tasks for non-expert users.",
+          1
+        ]
+      ],
+      "36": [
+        [
+          "Multi-Label Text Classification for Document Categorization.\nThis topic involves the use of supervised text classification techniques to categorize documents with multiple relevant labels from a large label set. Various methods such as Extreme Multi-Label Text Classification and Hierarchical Multi-Label Text Classification have been proposed for this task, and encoding labels as vectors with fixed sizes has been a common practice. Relevant metadata and label hierarchies can also be utilized to improve the accuracy of classification. Recently, deep learning-based methods have shown promise in this domain.",
+          1
+        ]
+      ],
+      "37": [
+        [
+          "Text Style Transfer in Natural Language\n\nThis topic revolves around the task of rephrasing a given natural language text in a different style while retaining its semantic meaning. Various approaches, both supervised and unsupervised, have been proposed to tackle this challenge. However, the lack of parallel corpora and auto-evaluation methods remain significant issues, especially for unsupervised style transfer systems. The existing approaches often focus on sentence-level style transfer without taking into account the contextual information. The style is generally described with attributes such as formality or authorship. The ultimate goal is to generate paraphrased sentences that reflect the desired style, which can be useful in a variety of applications, such as conversational agents, content creation, and language teaching.",
+          1
+        ]
+      ],
+      "38": [
+        [
+          "Question Generation Models and Automatic Question Generation\n\nThis topic discusses the importance of question generation in both human and machine intelligence, and how it can improve knowledge acquisition, question-answering, and machine reading comprehension tasks. It focuses on automatic question generation, which aims to generate questions from a text passage that can be answered by certain sub-spans of the given passage. The documents in this subset cover the use of sequence to sequence neural models for question generation, as well as the traditional heuristic rules for transforming a sentence into related questions. Additionally, the topic introduces the task of consecutive question generation, where a set of logically related question-answer pairs are generated to understand a whole passage with comprehensive consideration given to accuracy and coverage. The ultimate goal of this topic is to improve the training of question answering systems, chatbot conversations, and provide assessment materials for educational purposes.",
+          1
+        ]
+      ],
+      "39": [
+        [
+          "This topic revolves around the analysis of written texts to identify the authorship and writing style of various authors. It includes techniques such as authorship attribution and author profiling, as well as the study of literary texts and writing styles in general. The use of computational methods and natural language processing is emphasized in analyzing texts and identifying unique features of an author's writing. The topic also touches on ethical considerations and challenges related to maintaining anonymity in digital communication.",
+          1
+        ]
+      ],
+      "40": [
+        [
+          "This topic discusses various techniques and methods for learning and generating sentence embeddings, which represent sentences as fixed dense vectors, in natural language processing (NLP). The topic covers both unsupervised and supervised learning methods for building sentence embeddings, including contrastive learning frameworks like DiffCSE. The applications of sentence embeddings in enhancing performance across different NLP tasks and systems are also discussed. Lastly, the topic explores the limitations of existing contrastive methods and the need to improve the quality of sentence representations to achieve better performance in downstream tasks.",
+          1
+        ]
+      ],
+      "41": [
+        [
+          "Language identification and processing in code-switched and mixed-text data across monolingual and multilingual language pairs. The challenge of identifying and processing code-switching and mixed-text data in natural language processing, especially in intra-sentential data, is an understudied phenomenon. The topic includes the increasing adoption of code-switching on social media platforms and the need for extensive study of code-mixed natural language processing. The use of  multilingual or sociolinguistic configurations in code-switching is frequently observed nowadays. The data on code-switching differs so radically from benchmark corpora used in the NLP community that the application of standard techniques is challenging.",
+          1
+        ]
+      ],
+      "42": [
+        [
+          "This topic is about generating stories, narratives, and text using computer algorithms. The goal is to create a system that can automatically generate coherent and engaging stories from different input sources, such as images or semantic representations of story structures. This topic also explores generating stories in different languages and the use of deep learning algorithms to improve the quality and diversity of generated stories. The potential applications of story generation include interactive story systems and personalized content creation.",
+          1
+        ]
+      ],
+      "43": [
+        [
+          "Discourse analysis and parsing, discourse relations, and discourse structures are key areas of research in natural language processing. This topic explores the use of annotated corpora and parsers for tasks such as identifying implicit discourse relations, recognizing discourse markers, and evaluating different discourse frameworks such as PDTB and RST. The Penn Discourse Treebank is a commonly used resource in this field, and recent work has also focused on building similar corpus for other languages such as Turkish. Overall, this topic highlights the importance of understanding the structure and organization of discourse in textual communication.",
+          1
+        ]
+      ],
+      "44": [
+        [
+          "The use of pre-trained and deep-learning-based programming language models for automated software engineering tasks, including code understanding, code generation, code representation learning, and code translation between programming languages and natural language. Research is focused on generating high-level natural language descriptions for code methods and functions, as well as open-domain code generation from natural language intents.",
+          1
+        ]
+      ],
+      "45": [
+        [
+          "Paraphrase generation and detection in natural language processing \n\nThis topic focuses on the generation and detection of paraphrases in natural language processing. Paraphrase generation is the task of generating a sentence or phrase that has the same meaning as another sentence or phrase. This has many downstream applications in various tasks. However, the problem of lack of diversity in generated paraphrases still exists. Paraphrase detection and identification are crucial in NLP, as they help in identifying whether two sentences have the same meaning or not. The topic also discusses the challenges associated with document-level paraphrase generation and the necessity of using separate definitions of paraphrase for identification and generation tasks. Lastly, the difficulty of obtaining annotated paraphrase pairs for supervised paraphrase models is also addressed.",
+          1
+        ]
+      ],
+      "46": [
+        [
+          "Topic: This topic focuses on the use of reinforcement learning (RL) in training artificial agents to understand and interact with natural language instructions in text-based scenarios, such as computer games and navigation tasks. The use of RL allows these agents to learn and improve their performance over time through trial and error. The documents provided in this subset showcase various approaches to training agents for text-based tasks using RL, including exploration and imitation-based techniques, as well as the use of symbolic modules to augment agent abilities. These approaches have resulted in state-of-the-art performance in playing text-based computer games and navigating through 3D environments based on natural language instructions.",
+          1
+        ]
+      ],
+      "47": [
+        [
+          "Understanding public sentiment and mental health impacts related to the Covid-19 pandemic through social media analysis.",
+          1
+        ]
+      ],
+      "48": [
+        [
+          "Entity linking and disambiguation using entity embeddings and retrieval from knowledge bases. This topic explores various approaches to the task of entity linking and disambiguation in natural language processing, which involves linking mentions of entities in text to their corresponding entities in a knowledge base. It discusses challenges such as lexical ambiguity, incomplete knowledge bases, and overfitting of entity linking models to specific domains. The topic also focuses on the use of entity embeddings and retrieval techniques from knowledge bases to improve the accuracy of entity linking approaches. Finally, it examines how changes and evolution in entities over time impact entity linking performance.",
+          1
+        ]
+      ],
+      "49": [
+        [
+          "This topic explores the generation of poetic text using artificial intelligence techniques, with a focus on classical Chinese poetry. The use of pre-trained models such as BART is proposed as a means of generating qualified classical poetry. Training deep learning systems on a corpus of poems can teach them to generate poetry with a particular style of language. Some studies in this topic also incorporate linguistic features of poetry such as style and sentiment. Researchers are also interested in analyzing the poetic style of previous dynasties of classical Chinese poetry. Overall, this topic aims to generate quality poetry and understand the cultural and artistic value of classical Chinese verse.",
+          1
+        ]
+      ],
+      "50": [
+        [
+          "This topic focuses on the development and improvement of image captioning systems using visual and textual data for various applications. It explores the use of vision and language pre-training models, as well as the challenges of evaluation and unsupervised generation of captions. One of the specific areas of focus is the creation of image captions for the visually impaired, while also addressing the importance of captions meeting communicative goals. Additionally, the topic discusses the multimodal nature of image captioning and the need for aligning cross-modal semantics using language models.",
+          1
+        ]
+      ],
+      "51": [
+        [
+          "Natural Language Inference (NLI) and Recognizing Textual Entailment (RTE) are tasks in natural language processing aimed at predicting the entailment relationship between a pair of sentences (premise and hypothesis). This topic includes the development and evaluation of learning models for NLI, as well as the proposal of a hypothesis-only baseline for diagnosing NLI. Additionally, this topic involves the recognition of whether one piece of text is textually entailed by another, and the exploration of entailment models and logical inference in NLI.",
+          1
+        ]
+      ],
+      "52": [
+        [
+          "This topic is centered around the task of keyphrase extraction and generation, which involves identifying and generating textual units that summarize the main topics of a given document. Keyphrases can either be present in the source text or absent but highly related to the content. The documents in this topic highlight the methods and challenges involved in performing keyphrase extraction and generation tasks.",
+          1
+        ]
+      ],
+      "53": [
+        [
+          "Text Simplification and Paraphrasing Techniques in Machine Translation\n\nThis topic covers research and development in the field of text simplification, which aims to make written information easier to understand using techniques such as paraphrasing and sentence simplification. The documents in this subset specifically focus on the use of machine learning models in sentence-level simplification, the challenges of creating high-quality simplification datasets, and the development of multilingual unsupervised sentence simplification systems. The ultimate goal of this topic is to improve access to information by making it more accessible to a wider audience.",
+          1
+        ]
+      ],
+      "54": [
+        [
+          "Empathetic Dialogue Generation for Conversational AI Systems. This topic focuses on the development of dialogues for conversational agents and chatbots that exhibit empathy towards users, by tracking and understanding users' emotions, and responding appropriately with empathetic reactions. The documents in this topic address various aspects of empathetic dialogue generation, including detecting and modelling emotions, proposing better response generation methods, and exploring ways for implementing empathy in dialogue systems for more human-like interactions.",
+          1
+        ]
+      ],
+      "55": [
+        [
+          "This topic explores the relationship between depression, mental health disorders, and social media use among users of platforms like Twitter and Facebook. It discusses how natural language processing (NLP) techniques can be used to classify social media posts as indicative of mental or psychiatric disorders and how this could potentially aid in diagnosis and treatment. The topic also highlights the prevalence of mental illnesses worldwide and the need for large-scale labeled datasets to train machine learning models to identify these conditions from user-generated content on social media. Moreover, the topic touches upon the valuable resource of social media to identify suicidal ideation and assess suicide risk, particularly on Reddit's topic-based communities.",
+          1
+        ]
+      ],
+      "56": [
+        [
+          "Chinese Word Segmentation and its Models\n\nThe topic focuses on Chinese word segmentation, which is a fundamental task in computational linguistics and natural language processing, and involves the division of a sentence into individual words. Researchers have debated the importance of this task, and some have proposed new approaches to the problem, such as multi-criteria learning and graph-based segmentation. The prevalent approaches to this task rely on the use of Bi-LSTM neural networks, but it has some drawbacks in parallel computing and training with few labeled sentences. The topic also discusses the use of part-of-speech tagging and speech tasks in Chinese word segmentation, and the need for Chinese word-based models to improve the accuracy of the segmentation.",
+          1
+        ]
+      ],
+      "57": [
+        [
+          "This topic focuses on the importance of citations in scientific publications, including scientific papers, scholarly articles, and academic documents. The continuous growth of scientific literature and the high volume of published papers make it challenging to analyze them manually, and thus, machine reading and automated analysis of the scientific literature are proposed. One critical aspect of citation analysis is identifying their intent, which can help in understanding the impact of scholarly papers and building faceted search for digital library search engines. Qualitative analysis of citations provides deeper insights into the impact of scientific literature in the community.",
+          1
+        ]
+      ],
+      "58": [
+        [
+          "Neural Language Models and Recurrent Neural Networks (RNNs) for Natural Language Processing (NLP) Tasks, with a focus on LSTM-based models. The effectiveness of these models in capturing and performing tasks related to syntactic dependencies and subject-verb agreement is analyzed, as well as the ability of RNNs to acquire syntax across languages with varying typological properties. The study also aims to elucidate the mechanisms by which these models accomplish these behaviors.",
+          1
+        ]
+      ],
+      "59": [
+        [
+          "This topic focuses on the use of metaphors, figurative language and embeddings in natural language processing (NLP) and linguistic language models. It includes discussions on the challenges of metaphor generation, idiomatic expressions, metaphor detection and the emotional impact of metaphors compared to literal expressions. Additionally, the topic touches on the distinction between novel and conventional metaphors and how metaphorical expressions evolve over time.",
+          1
+        ]
+      ],
+      "60": [
+        [
+          "Semantic role labeling and its relationship with syntactic information and parsing.\n\nThis topic explores the field of Semantic Role Labeling (SRL), which is the process of identifying the semantic structure of a sentence based on its predicate-argument relationships. The documents in this subset highlight the importance of incorporating syntactic information, such as syntactic trees and dependency parsing, in SRL models and their impact on performance. Additionally, the papers discuss different approaches to SRL, including end-to-end models and reducing the task to dependency parsing, and the challenges associated with low-resource languages and domains. Overall, this topic examines the intersection of semantics and syntax in natural language processing and the importance of considering both for SRL tasks.",
+          1
+        ]
+      ],
+      "61": [
+        [
+          "Data privacy concerns and privacy-preserving techniques for language models in processing text data, including issues with anonymization and personal information. The challenge of protecting private data in natural language processing (NLP) models is highlighted, as well as the importance of complying with data protection laws such as GDPR. The use of federated learning and differential privacy as potential solutions is also discussed. Overall, this topic emphasizes the need for effective privacy preservation in the context of text data.",
+          1
+        ]
+      ],
+      "62": [
+        [
+          "This topic concerns the detection and analysis of semantic changes in language over time using lexical semantic word representations and semantic word embeddings. It involves exploring how words acquire new meanings and lose old senses, as well as how new words are coined or borrowed to serve different purposes. The topic also covers the challenges in developing models of language and cultural evolution due to the scarcity of historical data on word meanings. Research in this field includes the examination of semantic differences between specific words in different corpora from different time periods, as well as the development of various procedures for simulating lexical semantic change and assessing existing models.",
+          1
+        ]
+      ],
+      "63": [
+        [
+          "This topic is about the use of various types of word embeddings, including bilingual word embeddings, cross-lingual embeddings, lingual word embeddings, and monolingual embeddings, for languages. It covers the use of unsupervised cross-lingual word embeddings, as well as offline methods that train embeddings in different languages and map them to a shared space through linear transformations. The topic also discusses recent advances in cross-lingual word embeddings and their applications in bilingual lexicon induction and cross-lingual transfer of word embeddings. Overall, this topic focuses on the use of word embeddings for representing and transferring knowledge across languages.",
+          1
+        ]
+      ],
+      "64": [
+        [
+          "This topic focuses on the analysis and understanding of political discourse, news media, and political ideology from a computational perspective. It includes issues such as political stance prediction for news articles, identifying bias in news media and political actors, modeling ideological perspectives of political actors, and the impact of digital campaigning on influencing public opinion. The goal of this topic is to shed light on the characteristics of digital political campaigns, identify and mitigate the influence of echo chambers, and improve our understanding of the polarization of political ideologies.",
+          1
+        ]
+      ],
+      "65": [
+        [
+          "This topic focuses on the development and application of dialogue generation and dialogue systems for question answering and conversational purposes in the medical field, particularly on assisting doctors and patients with diagnosis, treatment, and consultation. The use of natural language processing and conversational agents have attracted attention, especially with the rise of the COVID-19 pandemic. This topic also explores the challenges and limitations in developing domain-specific and fine-grained methods for answer retrieval in large-scale information retrieval.",
+          1
+        ]
+      ],
+      "66": [
+        [
+          "This topic focuses on mathematical reasoning and problem solving, particularly in the context of solving math word problems using equations. It also explores the use of natural language processing and machine learning to automate the process of solving math word problems and the challenges in accurately understanding the natural language texts to extract relevant math expressions. The importance of mathematical equations in scientific communication and the difficulties faced by students in reading and understanding them are also discussed within this topic.",
+          1
+        ]
+      ],
+      "67": [
+        [
+          "Sentiment Analysis in Financial News for Predicting Stocks\nDescription: This topic involves the use of natural language processing (NLP) techniques to analyze the sentiment of financial news headlines for predicting and forecasting stocks. Analysts in various disciplines, such as computer science, statistics, economics, finance, and operations research, are interested in this topic as it can help them make better decisions in planning and investing. The topic includes approaches for explainability in financial analysis, using the Pearson correlation coefficient to establish a relationship between sentiment analysis and stock prices. The challenge in this topic is to handle the uncertainty of the stock market and natural language understanding from a machine's perspective. The topic also explores how sentiment in financial publications can reflect market conditions during special events such as the 2020 COVID-19 pandemic.",
+          1
+        ]
+      ],
+      "68": [
+        [
+          "This topic pertains to table-based structured data and its analysis through various specialized models and pre-training methods. It highlights the importance of table structure understanding and table reasoning skills for data analysis. Furthermore, the topic relates to the development of transformer-based table question answering (QA) systems that can handle natural language questions and massive table corpus. The recent advances in transformer models and their pre-training on open-domain corpora have significantly improved the accuracy of table QA systems. Additionally, the topic emphasizes the growing interest in table pre-training frameworks that can be applied to a vast number of tables collected from different sources for data analysis.",
+          1
+        ]
+      ],
+      "69": [
+        [
+          "Evaluating text readability and complexity for language learners, with a focus on English as a second language (ESL). This includes the identification of linguistic features, use of corpus text analysis, and text simplification techniques to facilitate effective learning. Automatic readability assessment using natural language processing (NLP) is an important tool in education, aiding in the selection of appropriate reading materials for all levels of proficiency. Proper identification of reading materials' grade levels is crucial in the language learning process. Current studies explore the use of modern NLP approaches in English readability assessment and Persian language, which poses unique challenges. Traditional language-dependent formulas are being updated to consider more text characteristics.",
+          1
+        ]
+      ],
+      "70": [
+        [
+          "Visually Rich Document Understanding with OCR and Information Extraction\n\nThis topic focuses on the development of techniques to understand and extract information from visually rich documents. These documents may include structured and unstructured digital documents, as well as scanned documents and images. To achieve this goal, deep neural networks that are developed for computer vision are often utilized to recognize and segment the layout of documents. Pre-training techniques, which involve the systematic mining and utilization of layout-centered knowledge, have also been utilized successfully in recent years. However, most existing methods neglect the multi-modal information present in documents, including text, layout, and image, and instead focus solely on text-level manipulation. The ultimate aim of this topic is to be able to parse visually-rich documents into structured machine-readable formats for downstream applications.",
+          1
+        ]
+      ],
+      "71": [
+        [
+          "This topic explores the relationship between human language processing and the brain, specifically focusing on how language comprehension is represented and processed. Various aspects of language models, neuroscience, cognition, and neural responses are discussed, with a particular emphasis on understanding the alignment between language stimuli and detailed brain processes. The efficacy of task-specific learned language models and the use of EEG signals in interpreting human language understanding are also explored. Moreover, the potential of linking computational natural language processing models and neural responses in the human brain is discussed as a means of disentangling the neural representations underlying language perception. Finally, the use of word embeddings, recurrent neural networks and transformers is investigated in correlating these language models to brain activity recorded during language processing.",
+          1
+        ]
+      ],
+      "72": [
+        [
+          "This topic focuses on language recognition, translation, and understanding, with a particular emphasis on sign languages. The documents in this topic discuss various aspects of automatic sign language recognition and translation, including the use of phonology to train models for isolated sign language recognition, the potential for automatic sign language transcription to enhance communication between hearing disability and hearing majority communities, and the use of machine translation to generate spoken language translations from sign language glosses. Additionally, this topic explores the intersection of computer vision, linguistics, and machine translation in the field of automatic translation from signed to spoken languages.",
+          1
+        ]
+      ],
+      "73": [
+        [
+          "This topic revolves around the task of Visual Question Answering (VQA) which combines natural language processing and computer vision to provide accurate answers to questions about given images. It explores the challenges associated with answering questions about visuals and the usefulness of decomposing complex questions into simpler sub-questions. The domain of joint vision-language understanding and reasoning in VQA models has garnered significant attention, and the use of this technology for real-world scenarios, such as helping users with visual impairments, has also been explored. English is a resource-rich language that has witnessed various developments in datasets used for VQA models.",
+          1
+        ]
+      ],
+      "74": [
+        [
+          "Dataset Biases in Natural Language Understanding (NLU) Models and De-biasing Methods\n\nThis topic focuses on the issue of dataset biases in natural language understanding (NLU) models and their impact on the performance of these models in out-of-distribution datasets. The documents highlight that NLU models tend to rely on spurious correlations (dataset biases) to achieve high performance on specific datasets but perform poorly on datasets outside the training distribution. Several recent studies indicate that these biases can cause NLU models to fail to learn the underlying task, resulting in poor generalization. The documents also discuss various debiasing methods that have been proposed to overcome this issue, with the aim of improving NLU models' performance on out-of-distribution datasets. Overall, this topic highlights the importance of addressing dataset biases to improve the robustness and reliability of NLU models.",
+          1
+        ]
+      ],
+      "75": [
+        [
+          "Dialogue Systems and Grounded Dialogue with Multimodal Context\n\nThis topic focuses on dialogue systems and their ability to understand dynamic visual scenes in order to engage in conversations with users about the objects and events around them. The aim is to develop scene-aware dialog systems for real-world applications by integrating state-of-the-art technology. One of the key challenges in this area is to make conversational agents visually perceptive to the physical world, which is necessary for them to exhibit human-like intelligence. The proposed solution is image-grounded conversation, which explores the multimodal dialogue models that depend on retrieval-based methods and generation methods. Another important aspect of this topic is developing video-grounded dialogue systems, where a dialogue is conducted based on visual and audio aspects of a given video. This area of research is significantly more challenging than traditional image or text-grounded dialogue systems, because of the complexities of the feature space involved. Overall, the topic explores how dialogue systems can be made more effective and intelligent by incorporating multimodal dialogue models and grounded dialogue with visual and audio context.",
+          1
+        ]
+      ],
+      "76": [
+        [
+          "Statistical Machine Translation and Quality in Translation Systems\n\nThis topic discusses Statistical Machine Translation (SMT), which is currently dominating the Machine Translation research. There is an increasing interest in SMT due to political and social events in the world. Work has been done to compare the performance of SMT and Rule-Based Machine Translation (RBMT) systems on English-Indian and Indian-Indian language translations. Further, the creation of lexical resources for Machine Translation between English and Hindi has been explored. The main objective of this topic is to improve the quality of Machine Translation systems and translation models by using various Natural Language Processing (NLP) techniques and comparing their results.",
+          1
+        ]
+      ],
+      "77": [
+        [
+          "Multimodal Machine Translation with Neural Machine Translation (NMT) and Evaluation Task\n\nThis topic revolves around the application of neural machine translation (NMT) and multimodal translation models in improving machine translation quality through the integration of visual information. The focus is on enhancing text-only translations with additional features drawn from images, with an interest in developing algorithms suitable for various translation tasks - visual-textual, textual-visual, or purely textual. The evaluation task focuses on assessing the effectiveness of the multimodal neural machine translation system in comparison to monomodal neural machine translation systems. The dataset of interest includes text and images that allow for English-Hindi multimodal machine translation.",
+          1
+        ]
+      ],
+      "78": [
+        [
+          "Geolocation and Geographic Information in Natural Language Processing (NLP) and Location-Based Services (LBS)\n\nThis topic pertains to the use of geographic information, including maps, location data, and city names, in natural language processing and location-based services. The documents within this topic discuss the use of pre-trained models for geographic information prediction, analysis of spatial discourse on social media, and the linking of geographic locations to natural language text data, among other related subjects. The topic highlights the importance of geographic information in applications such as navigation, disaster response, and Twitter analysis.",
+          1
+        ]
+      ],
+      "79": [
+        [
+          "Large Language Models and their Reasoning Abilities with Chain-of-Thought Prompts\nThis topic explores the capabilities of Large Language Models (LLMs) in performing multi-step reasoning tasks, specifically with the aid of Chain-of-Thought (CoT) prompts. These prompts encourage the LLM to generate intermediate reasoning steps towards an answer, showing remarkable reasoning skills and the ability to rely on external tools for computation. This is a significant development in the sub-field of natural language processing (NLP), as LLMs continue to be widely used as excellent few-shot learners with task-specific exemplars. The topic also delves into existing benchmarks that measure reasoning ability indirectly through evaluating downstream task accuracy.",
+          1
+        ]
+      ],
+      "80": [
+        [
+          "This topic focuses on automated essay scoring and its application in assessing the language abilities of individuals. It explores the use of deep learning-based natural language processing techniques to grade essays, replace human raters, and provide formative feedback to guide essay revisions. The documents in this subset describe the challenges of current AES systems, the growing use of automated scoring engines, and efforts to improve the reliability and usefulness of AES.",
+          1
+        ]
+      ],
+      "81": [
+        [
+          "Extraction and labeling of tweets related to emergencies and natural disasters on social media for domain adaptation and crises management.",
+          1
+        ]
+      ],
+      "82": [
+        [
+          "The application of graph neural networks and graph convolutional networks for text classification and embedding representations in natural language processing.",
+          1
+        ]
+      ],
+      "83": [
+        [
+          "This topic pertains to the process of linguistic annotation and the use of annotation tools to add descriptive or analytic notations to raw language data, which can be in the form of time functions (audio, video, physiological recordings) or text. The topic also covers the challenges of creating linguistic annotations, including the involvement of multiple people and stages, as well as the difficulties in locating and reusing existing linguistic resources and tools.",
+          1
+        ]
+      ],
+      "84": [
+        [
+          "Entity alignment and representation in knowledge graphs using entity embeddings and semantic embeddings. This topic focuses on the problem of merging multiple knowledge graphs by identifying equivalent entities and aligning them to enrich the knowledge representations. Dangling entities and growth of knowledge graphs are important challenges that need to be addressed. Embedding-based techniques using entity and semantic embeddings are being explored to represent and align entities across knowledge graphs efficiently.",
+          1
+        ]
+      ],
+      "85": [
+        [
+          "This topic pertains to psycholinguistic features, psychometric assessment, and psychology related to personality traits. The topic discusses the recognition of personality traits from text-based analyses and the use of computational models for predicting personality. It also includes studies on cross-cultural perception of personality traits and the correlation between fundamental Big Five personality traits and linguistic features. The topic showcases a diverse range of approaches to studying personality across multiple languages.",
+          1
+        ]
+      ],
+      "86": [
+        [
+          "This topic is focused on detecting and predicting Alzheimer's disease using linguistic and psycholinguistic features in human speech. The documents in this subset highlight the importance of early diagnosis for Alzheimer's disease and the potential of using deep learning and speech classifiers for automatic screening. Additionally, the topic covers research related to the characteristics of language changes in patients with Alzheimer's disease, as well as the transferability of acoustic features across languages for prediction purposes. Overall, this topic is relevant to clinical and technological advances in the early detection and prevention of Alzheimer's disease in the elderly population.",
+          1
+        ]
+      ],
+      "87": [
+        [
+          "This topic explores the construction and expansion of taxonomies, which are hierarchical structures used in various knowledge-rich applications. It discusses the challenges of manual taxonomy curation and the increasing demand for automatic methods. The focus is on extracting hypernym-hyponym entity pairs and adding new concepts to existing taxonomies as data and business scope grow. The role of taxonomies is also highlighted in machine intelligence and knowledge representation for tasks such as question answering and web search. The topic emphasizes the importance of taxonomies in organizing knowledge and facilitating downstream tasks.",
+          1
+        ]
+      ],
+      "88": [
+        [
+          "Active Learning for Text Classification Using Labeled Training Data\n\nThis topic explores the use of active learning techniques for improving the efficiency of supervised machine learning models in text classification tasks, specifically for natural language processing. Active learning is an iterative process that targets labeling specific data samples to build a classification model, which results in significant cost savings compared to labeling the entire dataset. The focus is on reducing the dependence on large amounts of labeled training data through the use of active learning strategies. The documents in the subset discuss the promise and challenges of active learning in this context, including its applicability to real-world problems and the limitations of existing uncertainty-based criteria. The potential benefits of active learning for mitigating the cost and time needed for data labeling and model training are also explored.",
+          1
+        ]
+      ],
+      "89": [
+        [
+          "Summarization models for sentiment classification and text summarization task \n\nThis topic focuses on the development of summarization models for sentiment classification and text summarization tasks. Specifically, it involves generating summaries that reflect popular subjective information expressed in multiple online reviews, with a focus on product reviews. The aim is to extract useful information and identify important opinions from customer reviews, while generating summaries that are concise and easy to understand. The topic also covers abstractive summarization techniques and the development of summarization systems that can be applied to various platforms. Overall, the goal is to develop efficient and effective methods for summarizing large amounts of text data, particularly in the context of sentiment classification and text summarization tasks.",
+          1
+        ]
+      ],
+      "90": [
+        [
+          "Study and analysis of emojis, emoticons, and symbols in text and image-based communication, including their usage for sentiment analysis, and the development of embedding models to measure similarity and understand communicative intent.",
+          1
+        ]
+      ],
+      "91": [
+        [
+          "This topic encompasses the generation of natural language text from tables, with an emphasis on logical table-to-text generation. It discusses the challenges and limitations of neural table-to-text generation models and proposes the use of large-scale pretrained language models as a solution. The topic also explores the inadequacy of modeling table representation in one dimension and discusses controlled table-to-text generation. Finally, it highlights the importance of generating faithful descriptions that accurately translate knowledge triples to natural language.",
+          1
+        ]
+      ],
+      "92": [
+        [
+          "Domain adaptation and unsupervised domain adaptation in sentiment analysis and text classification models. This topic explores challenges and solutions related to generalizing machine learning models from a source domain to a target domain in sentiment analysis and text classification, particularly in situations where labeled data is scarce or non-existent in the target domain. It covers multi-source unsupervised domain adaptation, domain adversarial domain adaptation, class imbalance, and the use of only source domain annotations and unlabeled data for performance augmentation. The focus is on improving sentiment analysis and text classification performance, particularly in large-scale, real-world scenarios such as customer feedback analysis in social networks. The content includes both theoretical and practical aspects of adaptation techniques and their applications in cognitive computation.",
+          1
+        ]
+      ],
+      "93": [
+        [
+          "Word alignment and its importance in machine translation systems\n\nThis topic discusses the fundamental role of word alignment in machine translation systems and its significant contribution in creating language resources for low-resource languages. It highlights various word alignment methods including the supervised word alignment based on cross-language span prediction and the neural word alignment models. The topic also emphasizes the necessity of reference corpus for word alignment and the importance of sentence embeddings and translation models in automatic alignment and sentence alignment. Additionally, it showcases recent findings on the feasibility and improvement of unsupervised word translation through the use of visual observations.",
+          1
+        ]
+      ],
+      "94": [
+        [
+          "This topic explores the field of linguistics, particularly focusing on language families, dialects, and their relationships using methods such as Levenshtein distance and phylogenetic analysis. It also discusses the Indo-European language family and its root age. The topic includes various studies analyzing the syntactic parameters and name statistics of different languages.",
+          1
+        ]
+      ],
+      "95": [
+        [
+          "This topic revolves around the use of patent and intellectual property data for innovation research and patent analysis. It covers various techniques such as automatic classification, vector space models, and patent landscaping to extract valuable information from patent documents. Other relevant aspects include prior art, invention, knowledge graph, NLP, and domain adaptation for optimizing patent analysis, protection, and infringement risk avoidance.",
+          1
+        ]
+      ],
+      "96": [
+        [
+          "This topic explores various aspects of human language and natural language evolution, with a focus on referential games and their use in studying emergent communication between artificial agents. It also investigates the role of compositionality in artificial languages, the learning and understanding process in humans, and the use of deep learning models for natural language processing. The documents in this subset of the topic cover topics such as representation of visual input in referential games, communication with discrete symbols, and the use of grounded learning environments for neural agents in language acquisition.",
+          1
+        ]
+      ],
+      "97": [
+        [
+          "Graph neural networks and text generation from graph-based data\n\nThis topic discusses the challenges of generating text from graph-based data structures such as Abstract Meaning Representation (AMR). Pretrained language models (PLMs) have been used to encode and decode the graph structure and generate text. The use of graph neural networks for graph representation learning is also explored. The documents in this topic focus on novel approa",
+          1
+        ]
+      ],
+      "98": [
+        [
+          "Ethics, human values, and judgment in large language models and NLP\n\nThis topic investigates the relationship between language use, moral sentiment change, and ethical judgment. It focuses on the potential biases and insensitive viewpoints that may be reproduced by large language models (LLMs) and the implications for ethical decision-making. The documents in this subset discuss the need to equip machines with the ability to learn ethical and moral choices, the importance of contextual information for common sense moral reasoning, and the potential erosion of user trust in the moral integrity of conversational agents due to insensitive or incoherent viewpoints. The topic highlights the role that language models and NLP can play in shaping human values and judgments and the ethical considerations that must be taken into account when developing and deploying these technologies.",
+          1
+        ]
+      ],
+      "99": [
+        [
+          "This topic discusses the automatic extraction and manual annotation of abbreviations and acronyms from unstructured texts and the challenges of disambiguating ambiguous acronyms. It includes discussions on the importance of recognizing and understanding acronyms in various contexts such as scientific papers, biomedical reports, and search engine queries. The topic also covers existing benchmarks and tools for acronym disambiguation.",
+          1
+        ]
+      ],
+      "100": [
+        [
+          "This topic explores various aspects of entity typing, including fine-grained entity typing, ultra-fine entity typing, and neural entity typing models. The documents discuss the challenges associated with traditional classification-based entity typing, such as the inability to assign entities to types beyond the predefined set, and the use of rich and ultra-fine sets of types for labeling not just named entity mentions, but also noun phrases including pronouns and nominal nouns. The state-of-the-art methods for ultra-fine entity typing involve the use of cross-encoder based architecture to predict extremely free-formed types of a given entity mention in context. Additionally, the topic investigates the effectiveness of box embeddings in embedding fine-grained entity types in high-dimensional spaces, and the utility of end-task fine-tuning for embeddings produced by pre-trained models.",
+          1
+        ]
+      ],
+      "101": [
+        [
+          "Modeling and Understanding Discourse Coherence in Text and Speech",
+          1
+        ]
+      ],
+      "102": [
+        [
+          "This topic is focused on the importance of part-of-speech (POS) tagging in the field of natural language processing (NLP) and the different methods used to achieve it. It covers the use of machine learning methods to annotate corpora with POS tags, the challenges in unsupervised POS tagging in low-resource languages, strategies for dealing with sparsity in inflected languages such as Turkish, the impact of training POS taggers on different domains, and the need for POS tagged corpora in African languages to support advanced NLP research. The topic also discusses different NLP resources and tools available for POS tagging and annotation.",
+          1
+        ]
+      ],
+      "103": [
+        [
+          "This topic revolves around the use of social media data corpus for detecting adverse drug reactions and analyzing pharmaceutical products. It explores the potential of natural language processing and BERT models in extracting health-related named entities, identifying the effectiveness of drugs, and monitoring drug abuse. The focus is on improving pharmacovigilance research by automating the detection of Adverse Drug Events (ADE) in social media and incorporating Twitter data in pharmacoeconomic studies. The topic includes some representative documents such as the Russian Drug Reaction Corpus (RuDReC) for partially annotated consumer reviews in Russian, the use of Twitter posts as important sources of patient-generated data, and the significant interest in mining social media messages for drug-related information.",
+          1
+        ]
+      ],
+      "104": [
+        [
+          "This topic discusses gender-related issues in machine translation, including gender agreement, translation accuracy, and translation quality. It highlights the potential for machine translation systems to reflect or amplify social biases, as well as the impact of gender bias in training data on neural machine translation. The topic also explores the challenges of gender neutrality in cross-lingual contexts and the importance of context for accurate translation of gendered language. Finally, the topic touches on the differences in how gender is expressed in different languages and the implications for machine translation.",
+          1
+        ]
+      ],
+      "105": [
+        [
+          "Topic: Job Information Extraction and Classification using Machine Learning and NLP.\nThis topic deals with the use of machine learning and natural language processing (NLP) approaches to extract and classify job information from various sources such as job postings and resumes. It focuses on techniques to automatically detect and match job titles, descriptions, and other relevant information to help job seekers and recruiters. The use of supervised learning and machine learning algorithms for text matching and classification is explored to aid in the matching of job vacancies with suitable candidates. Additionally, the topic also looks at the use of soft skills in job matching and how aggregated data obtained from job postings can provide insights into labor market demands.",
+          1
+        ]
+      ]
+    }
+  }
+}
\ No newline at end of file