Legal text retrieval serves as a key component in a wide range of legal text processing tasks such as legal question answering, legal case entailment, and statute law retrieval. The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents. Based on good representations, a legal text retrieval model can effectively match the query to its relevant documents. Because legal documents often contain long articles and only (...) some parts are relevant to queries, it is quite a challenge for existing models to represent such documents. In this paper, we study the use of attentive neural network-based text representation for statute law document retrieval. We propose a general approach using deep neural networks with attention mechanisms. Based on it, we develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. The methods are evaluated on datasets of different sizes and characteristics in English, Japanese, and Vietnamese. Experimental results show that: (i) Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages; (ii) Pretrained transformer-based models achieve better accuracy on small datasets at the cost of high computational complexity while lighter weight Attentive CNN achieves better accuracy on large datasets; and (iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE dataset, achieving the highest recall and F2 scores in the top-N retrieval task. (shrink)

Previous research on cross-situational word learning has demonstrated that learners are able to reduce ambiguity in mapping words to referents by tracking co-occurrence probabilities across learning events. In the current experiments, we examined whether learners are able to retain mappings over time. The results revealed that learners are able to retain mappings for up to 1 week later. However, there were interactions between the amount of retention and the different learning conditions. Interestingly, the strongest retention was associated with a learning (...) condition that engendered retrieval dynamics that initially challenged the learner but eventually led to more successful retrieval toward the end of learning. The ease/difficulty of retrieval is a critical process underlying cross-situational word learning and is a powerful example of how learning dynamics affect long-term learning outcomes. (shrink)

Case law retrieval is the task of locating truly relevant legal cases given an input query case. Unlike information retrieval for general texts, this task is more complex with two phases (legal case retrieval and legal case entailment) and much harder due to a number of reasons. First, both the query and candidate cases are long documents consisting of several paragraphs. This makes it difficult to model with representation learning that usually has restriction on input length. Second, (...) the concept of relevancy in this domain is defined based on the legal relation that goes beyond the lexical or topical relevance. This is a real challenge because normal text matching will not work. Third, building a large and accurate legal case dataset requires a lot of effort and expertise. This is obviously an obstacle to creating enough data for training deep retrieval models. In this paper, we propose a novel approach called supporting model that can deal with both phases. The underlying idea is the case–case supporting relation and the paragraph–paragraph as well as the decision-paragraph matching strategy. In addition, we propose a method to automatically create a large weak-labeling dataset to overcome the lack of data. The experiments showed that our solution has achieved the state-of-the-art results for both case retrieval and case entailment phases. (shrink)

Recently, the pandemic caused by COVID-19 is severe in the entire world. The prevention and control of crimes associated with COVID-19 are critical for controlling the pandemic. Therefore, to provide efficient and convenient intelligent legal knowledge services during the pandemic, we develop an intelligent system for legal information retrieval on the WeChat platform in this paper. The data source we used for training our system is “The typical cases of national procuratorial authorities handling crimes against the prevention and control (...) of the new coronary pneumonia pandemic following the law”, which is published online by the Supreme People’s Procuratorate of the People’s Republic of China. We base our system on convolutional neural network and use the semantic matching mechanism to capture inter-sentence relationship information and make a prediction. Moreover, we introduce an auxiliary learning process to help the network better distinguish the relation between two sentences. Finally, the system uses the trained model to identify the information entered by a user and responds to the user with a reference case similar to the query case and gives the reference legal gist applicable to the query case. (shrink)

In recent years, political theorists have increasingly turned their attention to the past in search of conceptual renovation in the present. While recourse to the past has been a recurring thread throughout the history of political thought, the overlapping concern of recent scholarship has been to revisit seemingly exhausted political concepts with the aim of repurposing them for contemporary political challenges and realities. The three edited collections under review – Positive Freedom, Perfektionismus der Autonomie and Rethinking Liberty Before Liberalism – (...) are distinguished by their thoughtful attempts at retrieving and politicizing the concepts of positive freedom, post-Kantian perfectionism, and neo-Roman liberty in contemporary political thought. These retrievals present promising avenues for theoretical innovation, along with the ever-present risk of diminishing interpretive returns. (shrink)

We conducted two eye-tracking experiments investigating the processing of the Mandarin reflexive ziji in order to tease apart structurally constrained accounts from standard cue-based accounts of memory retrieval. In both experiments, we tested whether structurally inaccessible distractors that fulfill the animacy requirement of ziji influence processing times at the reflexive. In Experiment 1, we manipulated animacy of the antecedent and a structurally inaccessible distractor intervening between the antecedent and the reflexive. In conditions where the accessible antecedent mismatched the animacy (...) cue, we found inhibitory interference whereas in antecedent-match conditions, no effect of the distractor was observed. In Experiment 2, we tested only antecedent-match configurations and manipulated locality of the reflexive-antecedent binding (Mandarin allows non-local binding). Participants were asked to hold three distractors (animate vs. inanimate nouns) in memory while reading the target sentence. We found slower reading times when animate distractors were held in memory (inhibitory interference). Moreover, we replicated the locality effect reported in previous studies. These results are incompatible with structure-based accounts. However, the cue-based ACT-R model of Lewis and Vasishth ( 2005 ) cannot explain the observed pattern either. We therefore extend the original ACT-R model and show how this model not only explains the data presented in this article, but is also able to account for previously unexplained patterns in the literature on reflexive processing. (shrink)

We present a comprehensive empirical evaluation of the ACT‐R–based model of sentence processing developed by Lewis and Vasishth (2005) (LV05). The predictions of the model are compared with the results of a recent meta‐analysis of published reading studies on retrieval interference in reflexive‐/reciprocal‐antecedent and subject–verb dependencies (Jäger, Engelmann, & Vasishth, 2017). The comparison shows that the model has only partial success in explaining the data; and we propose that its prediction space is restricted by oversimplifying assumptions. We then implement (...) a revised model that takes into account differences between individual experimental designs in terms of the prominence of the target and the distractor in memory‐ and context‐dependent cue‐feature associations. The predictions of the original and the revised model are quantitatively compared with the results of the meta‐analysis. Our simulations show that, compared to the original LV05 model, the revised model accounts for the data better. The results suggest that effects of prominence and variable cue‐feature associations need to be considered in the interpretation of existing empirical results and in the design and planning of future experiments. With regard to retrieval interference in sentence processing and to the broader field of psycholinguistic studies, we conclude that well‐specified models in tandem with high‐powered experiments are needed in order to uncover the underlying cognitive processes. (shrink)

Posthumous sperm retrieval is a complex and contentious issue that raises various ethical, legal, and social concerns. Policies regulating this practice vary globally, reflecting diverse normative approaches and cultural values. Israel, which had a permissive stance on the matter even before the October 7 attack, has seen a significant shift in policy since the attack and subsequent war, now allowing sperm retrieval based on parental request. This development, partly driven by a desire to honor fallen soldiers, adds new (...) complexity to the ethical discourse. This paper uses Israel's policy shift as a starting point to explore whether parents of the deceased should be granted a right to initiate the procedure, particularly in the context of war and national loss. The discussion navigates a web of competing interests and narratives that require careful untangling, including the deceased's autonomy and bodily integrity, the relatives’ desire for genetic continuity, the potential for conflict between relatives with opposing wishes, the welfare of potential offspring, and broader societal implications with special attention to the tension between honoring sacrifice and respecting individual reproductive choices. (shrink)

We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks. On the other hand, we explore the benefits from combining lexical features and latent features generated with neural networks. Our experiments show that lexical features and latent features generated with neural networks complement each other to improve the retrieval system performance. Furthermore, our experimental results (...) suggest the importance of case summarization in different aspects: using provided summaries and performing encoded summarization. Our approach achieved F1 of 65.6% and 57.6% on the experimental datasets of legal case retrieval tasks. (shrink)

Legal Case Retrieval not only involves the identification and selection of similar cases, but also encompasses an in-depth comparison and abstraction of their holdings, reasoning structures, and underlying legal principles, serving as a critical pillar of judicial digital transformation and the development of smart courts. With the rapid advancement of neural networks, deep learning, and pretrained language models, retrieval paradigms have gradually evolved from traditional keyword matching toward intelligent approaches such as semantics-based dense retrieval. Owing to the (...) high level of domain specificity and structural complexity of legal documents, case-based retrieval has long remained a focal research topic in this field. To leverage the strengths of traditional methods in capturing global semantics while fully exploiting the contextual modeling capabilities of pretrained language models, this paper proposes ALTER, A Lightweight Topic-awarE Representation legal case retrieval system. Specifically, ALTER incorporates topic modeling to enhance the global semantic representations learned by pretrained language models, and introduces a Co-Attention layer to explicitly facilitate bidirectional interactions between the topic encoder and the text encoder. In addition, a multi-label classification task is employed to guide the text encoder to implicitly learn topical information, resulting in topic-aware text representations. During the online retrieval stage, only the text encoder is used to construct the index, enabling efficient and lightweight inference while achieving joint matching between text and topics. Experimental results on the Chinese LCR dataset LeCaRD and the English LCR dataset COLIEE23-24 demonstrate that ALTER consistently achieves state-of-the-art performance across multiple evaluation metrics. (shrink)

Involuntary autobiographical memories (IAMs) and déjà vu are phenomena that occur spontaneously in daily life. IAMs are recollections of the personal past, whereas déjà vu is defined as an experience in which the person feels familiarity at the same time as knowing that the familiarity is false. We present and discuss the idea that both IAMs and déjà vu can be explained as natural phenomena resulting from memory processing and, importantly, are both based on the same memory retrieval processes. (...) Briefly, we hypothesise that both can be described as “involuntary” or spontaneous cognitions, where IAMs deliver content and déjà vu delivers only the feeling of retrieval. We map out the similarities and differences between the two, making a theoretical and neuroscientific account for their integration into models of memory retrieval and how the autobiographical memory literature can explain these quirks of daily life and unusual but meaningful phenomena. We explain the emergence of the déjà vu phenomenon by relating it to well-known mechanisms of autobiographical memory retrieval, concluding that IAMs and déjà vu lie on a continuum. (shrink)

_Retrieving Political Emotion _engages the reader in an excursion through our ancient Greek heritage to recover a concept of emotion useful for enriching political philosophy today. Focusing on _thumos_, Barbara Koziak reveals misinterpretations of the concept that have hampered recognition of its possibilities for normative theory. Then, drawing especially on Aristotle's construal of it as a general capacity for emotion and relating this to contemporary multidisciplinary work on emotion, she reformulates _thumos_ to provide a more adequate theory of political emotion, (...) as an antidote to the modern fixation on rational self-interest as the key to explaining political behavior. The book proceeds by recounting the way _thumos_ is used in Homer's _Iliad_ and Plato's _Republic_ and then showing how, while borrowing from both, Aristotle went substantially beyond them. From the _Nicomachean Ethics and Politics_ we can see the activity of _thumos_—how a person with good _thumos_ acts and through which institutions. Through the _Poetics_ we observe the characteristic disposition of _thumos_—what patterns and objects typify a person's emotional capacity in the best regime. Her reconstructed Aristotelian theory of political emotion allows Koziak in the concluding chapter to show how it can help us better understand political behavior today, as manifested in recent congressional debates on welfare reform, and through constructive engagement with feminist thinking on the "ethics of care" lead political theory to pay more attention to the importance of passions in political life. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Journal of Speculative Philosophy 17.1 (2003) 73-75 [Access article in PDF] Retrieving Experience: Subjectivity and Recognition in Feminist Politics. Sonia Kruks. Ithaca and London: Cornell University Press, 2001. Pp. xii + 200. $35.00 h.c. 0-8014-3387-8; $16.95 pbk. 0-8014-8417-0. Sonia Kruks' latest book, Retrieving Experience, is a valuable contribution to ongoing debates about the relevance of feminist philosophy in a period of relative political quietism. It also offers timely (...) reminders of historical continuities between phenomenological and postmodern conceptions of subjectivity and political engagement. Drawing on Merleau-Ponty, Beauvoir, Fanon, and the later Sartre, Kruks proposes to "explore women's embodied experiences" and to "ask whether they might not offer some means to help build affective bonds among women who are in many ways radically different from one another" (6). The book approaches this task by means of two secondary issues. First, should feminist thought be oriented by phenomenological or postmodernist theoretical strategies? Second, can women's embodied experience be the conceptual basis for political and epistemological practices that avoid the pitfalls of "identity politics" or an "epistemology of provenance"? The answer to both questions comes through the notion of praxis developed in Sartre's Critique of Dialectical Reason. By thinking of embodiment as praxis or work, a strategy implicit in Beauvoir's idea that "one is not born, but rather becomes, a woman." Kruks seeks to resolve the ethical affront of finding one's individuality reduced to the "generality" of a collective—the ethical affront to which feminists of various generations and theoretical persuasions must respond.Retrieving Experience explores the significance of recognition as a political norm. Identifying Sartre's Anti-Semite and Jew as a pivotal work in the history of identitarian theorizing, she situates recent appeals for race- and gender-based political mobilization in the United States with respect to the dialectical relationship between the Look, shame, and recognition existentialists observed in situations of anti-Semitic and colonial domination during and immediately after the Second World War. Legitimating knowledge gained under oppressive conditions formed an important element of African, Southeast Asian, and ultimately African-American and feminist resistance to cultural imperialism. However, Kruks warns, discourses of "recognition" risk placing the blame for group domination on individuals' face-to-face interactions while concealing the role of state and economic institutions in enabling social disdain to systematically damage a group's quality of life. Moreover, they suggest that the experience of subjugation alone justifies knowledge claims or counts, without further elaboration, as an act of political engagement. Kruks argues that knowers and actors need to identify conceptual and practical mediations enabling individual interactions, experiences, and strategies to prompt the recognition and transformation of [End Page 73] group situations. Sartre's notion of praxis, she suggests, explains how an individual can participate in a collectivity while acting autonomously or acquiescing to subjection.The most important passages in Retrieving Experience examine the affective aspects of the decision to take up a political identity or engage in political action. Going beyond Sartre's account of mediations, Kruks suggests that the sensory and emotional qualities of embodiment are themselves mediators, the products of praxis as well as its vehicles. Since any act of social knowledge involves the emotions, affect plays an important role in the process whereby individuals from different cultural, economic, and bodily situations learn about and shape one another's' environment through the act of knowledge. Individuals, then, may have a moral responsibility to learn about others in a way that gives them "sympathetic bodies." Drawing on Maria Lugones' social epistemology, Kruks argues that "world-traveling" or existing simultaneously in several communities or sets of power relations is a bodily act, but also a fully cultural, ethical act rather than a natural way of being. One is not born, but becomes, an embodied knower. Understanding female embodiment as a kind of work or "world-traveling" praxis enables feminist solidarity—"respectful recognition" to replace a historically implausible "sisterhood" or a distinct epistemological perspective as a regulative ideal for feminist politics.But how far can the... (shrink)

We present five experiments and simulation studies to establish late analogical abstraction as a new psychological phenomenon: Schema abstraction from analogical examples can revive otherwise inert knowledge. We find that comparing two analogous examples of negotiations at recall time promotes retrieving analogical matches stored in memory—a notoriously elusive effect. Another innovation in this research is that we show parallel effects for real‐life autobiographical memory (Experiments 1–3) and for a controlled memory set (Experiments 4 and 5). Simulation studies show that a (...) unified model based on schema abstraction can capture backward (retrieval) effects as well as forward (transfer) effects. (shrink)

Theories of autobiographical memory emphasise effortful, generative search processes in memory retrieval. However recent research suggests that memories are often retrieved directly, without effortful search. We investigated whether direct and generative retrieval differed in the characteristics of memories recalled, or only in terms of retrieval latency. Participants recalled autobiographical memories in response to cue words. For each memory, they reported whether it was retrieved directly or generatively, rated its visuo-spatial perspective, and judged its accompanying recollective experience. Our (...) results indicated that direct retrieval was commonly reported and was faster than generative retrieval, replicating recent findings. The characteristics of directly retrieved memories differed from generatively retrieved memories: directly retrieved memories had higher field perspective ratings and lower observer perspective ratings. However, retrieval mode did not influence recollective experience. We discuss our findings in terms of cue generation and content construction, and the implication for reconstructive models of autobiographical memory. (shrink)

The perspective of online dispute resolution is to develop an online electronic system aimed at solving out-of-court disputes. Among ODR schemes, eMediation is becoming an important tool for encouraging the positive settlement of an agreement among litigants. The main motivation underlying the adoption of eMediation is the time/cost reduction for the resolution of disputes compared to the ordinary justice system. In the context of eMediation, a fundamental requirement that an ODR system should meet relates to both litigants and mediators, i.e. (...) to enable an informed negotiation by informing the parties about the rights and duties related to the case. In order to match this requirement, we propose an information retrieval system able to retrieve relevant court decisions with respect to the disputant case description. The proposed system combines machine learning and natural language processing techniques to better match disputant case descriptions with court decisions. Experimental results confirm the ability of the proposed solution to empower court decision retrieval, enabling therefore a well-informed eMediation process. (shrink)

Blanchette and Dunbar have claimed that when participants are allowed to draw on their own source analogs in the service of analogical argumentation, retrieval is less constrained by surface similarity than traditional experiments suggest. In two studies, we adapted this production paradigm to control for the potentially distorting effects of analogy fabrication and uneven availability of close and distant sources in memory. Experiment 1 assessed whether participants were reminded of central episodes from popular movies while generating analogies for superficially (...) similar versus superficially dissimilar target situations. In Experiment 2, we modified this procedure to assess the retrieval of autobiographic memories, more familiar to participants than fictional episodes. Both studies revealed a strong effect of surface similarity on the retrieval of participants' own sources, thus suggesting that the superficial bias typically observed in experimental studies—and simulated by most computational models—does not originate in a lack of ecological validity. (shrink)

The concept of ‘relevance’ is crucial to legal information retrieval, but because of its intuitive understanding it goes undefined too easily and unexplored too often. We discuss a conceptual framework on relevance within legal information retrieval, based on a typology of relevance dimensions used within general information retrieval science, but tailored to the specific features of legal information. This framework can be used for the development and improvement of legal information retrieval systems.

The Retrieval-Induced Forgetting (RIF) paradigm is used to study how the repeated retrieval practice of particular memories impairs the retrieval of related memory traces. A study is reported where this automatic form of forgetting was investigated in a group of sexual-assault victims and a control group. Using a recognition-cued RIF task, the present study examined RIF with neutral, positive, negative and trauma-specific stimuli. Response time data showed that irrespective of previous trauma exposure, a RIF effect was observed (...) for neutral material, but not for emotional material. No differences in RIF between the trauma group and the control group were found. Inconsistencies with previous literature and the implications for emotional memory are discussed. (shrink)

There is a growing sense that many liberal states are in the midst of a shift in legal and political norms—a shift that is happening slowly and for a variety of reasons relating to security. The internet and tech booms—paving the way for new forms of electronic surveillance—predated the 9/11 attacks by several years, while the police’s vast use of secret informants and deceptive operations began well before that. On the other hand, the recent uptick in reactionary movements—movements in which (...) the rule of law seems expendable—began many years after 9/11 and continues to this day. One way to describe this book is an examination of the moral limits on modern police practices that flow from the basic legal and political tenets of the liberal tradition. The central argument is that policing in liberal states is constrained by a liberal conception of persons coupled with particular rule of law principles. Part I consists of three chapters that constitute the book’s theoretical foundation, including an overview of the police’s law enforcement role in the liberal polity and a methodology for evaluating that role. Part II consists of three chapters that address applications of the theory, including the police’s use of informants, deceptive operations, and surveillance. The upshot is that policing in liberal societies has become illiberal in light of its response to both internal and external threats to security. The book provides an account of what it might mean to retrieve policing that is consistent with the basic tenets of liberalism and the limits imposed by those tenets. [This is an uncorrected draft of the book's preface and introduction, forthcoming from Oxford University Press.]. (shrink)

Effective legal case retrieval is critical for fair and just adjudication of similar cases. Unfortunately, current methods fall short of understanding legal cases at a semantic level. To address the issue, this paper presents a BERT-based two-stage ranking model for Chinese legal case retrieval. The first stage uses the well-known BM25 ranking function to quickly retrieve top n case candidates from the candidate pool. In the second stage, firstly the fine-tuned BERT is used for semantic analysis of the (...) retrieved case candidates (i.e., obtained their embeddings) and then a ranking mechanism is used to re-rank the retrieved cases. We fine-tune the BERT model by integrating the point-wise method with auxiliary learning to better understand the deep semantics of a legal judgement text, and we further train the re-ranking neural network based on the fine-tuned BERT model in conjunction with the pair-wise method. Our extensive experiments reveal that the proposed method surpasses state-of-the-art methods on the Legal Case Retrieval Dataset (LeCaRD) by improving the ranking quality of retrieved legal cases, leading to more relevant cases appearing higher in the ranked results. (shrink)

This work explores provision-level retrieval and neural reranking for UK primary and secondary legislation. We introduce UK-StatuteCorpus, a corpus of recent UK Acts and statutory instruments from legislation.gov.uk, together with a 100-query evaluation set of practitioner-style questions whose graded relevance judgements distinguish legally operative, supporting and contextual provisions. Using BM25 and an MPNet-based dense retriever to build candidate sets, we evaluate ten neural rerankers, including transformer cross-encoders, a late-interaction reranker, an LLM-based listwise reranker and proprietary APIs. Across both sparse (...) and dense pools, neural reranking consistently improves normalized Discounted Cumulative Gain (nDCG) and Mean Reciprocal Rank (MRR) over first-stage retrieval. We further distil a proprietary Voyage reranker into a ModernBERT-based cross-encoder, Distilled-Voyage-ModernBERT, which approaches the teacher’s effectiveness and outperforms other open rerankers on our benchmark. Results are based on 100 expert-validated queries, each linked to three graded provisions from a single UK instrument, so they characterise single-instrument, provision-level retrieval over recent UK legislation. (shrink)

As artificial intelligence transforms legal practice, deploying Large Language Models effectively has become critical. While LLMs show promise across legal tasks, challenges around factual accuracy and domain-specific reasoning persist, particularly for citation prediction–where authoritative references carry binding legal force. We introduce the AusLaw Citation Benchmark, comprising 55k real-world Australian instances and 18,677 unique citations–the largest jurisdiction-specific dataset for this task. We systematically compare prompting, retrieval, fine-tuning, and hybrid strategies, including instruction-tuned models, sparse and dense retrieval, and re-ranker ensembles. (...) Our findings reveal that stand-alone generative models–whether general or law-specific–fail almost entirely, underscoring the risks of unaugmented deployment. Task-specific instruction tuning dramatically improves performance, BM25 outperforms dense embeddings in retrieval, and jurisdiction-specific pre-training surpasses larger but less targeted models. Hybrid approaches with trained re-rankers achieve the best results, yet a substantial 40% performance gap remains, exposing the persistent long-tail challenge in citation prediction. These results reframe assumptions about scale, retrieval, and fine-tuning, and establish a foundation for building reliable, jurisdiction-aware legal AI systems. For code, data, and models, see https://auslawbench.github.io/. (shrink)

Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental lexicon. Network measures at different levels of analysis (degree, network distance, and closeness centrality) were used to investigate the influence of network structure on picture naming accuracy and errors by people (...) with Anomic, Broca's, Conduction, and Wernicke's aphasia. Our results reveal that word retrieval is influenced by the multiplex lexical network structure in at least two ways—(a) the accuracy of production and error type on incorrect productions were influenced by the degree and closeness centrality of the target word, and (b) error type also varied in terms of network distance between the target word and produced error word. Taken together, the analyses demonstrate that network science techniques, particularly the use of the multiplex lexical network to simultaneously represent semantic and phonological relationships among words, reveal how the structure of the mental lexicon influences language processes beyond traditionally examined psycholinguistic variables. We propose a framework for how the multiplex lexical network approach allows for understanding the influence of mental lexicon structure on word retrieval processes, with an eye toward a better understanding of the nature of clinical impairments, like aphasia. (shrink)

The act of retrieving a memory has been found to suppress the recall of related memories, a phenomenon known as retrieval-induced forgetting (RIF). RIF is thought to involve an inhibitory mechanism of some kind. The purpose of the present study was to investigate the hypothesis that clinical depression might be associated with an abnormality in the mechanism underlying RIF. The RIF procedure was carried out on 21 clinically depressed participants and 21 normal controls. The depressed group demonstrated significantly lower (...) RIF scores than did the control group. However, there was no difference between the overall recall scores of the depressed and normal groups, indicating that the differences were confined to RIF and were not just a reflection of a general impairment of memory. The authors concluded that the RIF effect was deficient in clinically depressed individuals. Possible causal relationships between RIF and depression are discussed. (shrink)

As the number of legal cases continues to grow, legal case retrieval (LCR), which aims to identify relevant cases by a given query, plays a more and more important role in legal artificial intelligence (AI). Benefiting from the prosperity of advanced AI techniques, dense retrieval has made great progress in accomplishing the LCR task. However, as a domain-specialized task, building valid LCR systems still faces challenges whereas current studies meet with mixed success. First, the query is generally short (...) and compact, preventing key information from being fully utilized during retrieval. Second, relevance labels are insufficient or even unavailable, resulting in inadequate supervision in model training. Lastly, existing methods have weak generalization ability and are difficult to scale. To tackle these challenges, we propose a novel framework called _InstructLCR_. We first design an instruction scheme called _Content-aware Instruction_ to guide the instruction-following large language model (LLM) in generating full-content legal cases. Then, query and instruction are combined to form the input of LLM — we add no case example in the input to meet the zero-shot setting. The generated cases provide the retrievers with supplementary context. Aided by the additional contextual information, we can align the queries and candidate cases in a more accurate manner. Moreover, the lack of relevance labels is also alleviated as the generated contents provide more information on which the retrievers can determine the similarity of queries and candidate cases more easily. Experimental results on two Chinese criminal LCR benchmark datasets demonstrate that _InstructLCR_ can boost the retrieval performance by an average of 6.2%, with a maximum improvement of 19%. We have also obtained new state-of-the-art (SOTA) results by simply integrating _InstructLCR_ with existing models, without requiring any training or fine-tuning effort. (shrink)

This paper develops a novel psycholinguistic parser and tests it against experimental and corpus reading data. The parser builds on the recent research into memory structures, which argues that memory retrieval is content‐addressable and cue‐based. It is shown that the theory of cue‐based memory systems can be combined with transition‐based parsing to produce a parser that, when combined with the cognitive architecture ACT‐R, can model reading and predict online behavioral measures (reading times and regressions). The parser's modeling capacities are (...) tested against self‐paced reading experimental data (Grodner & Gibson, 2005), eye‐tracking experimental data (Staub, 2011), and a self‐paced reading corpus (Futrell et al., 2018). (shrink)

Aversive memories have the potential to impair one’s psychological well-being. It is desirable to reduce the anguish over such memories, as well as the chance that they will be retrieved. In two experiments, we investigated whether retrieval stopping reduces the distress elicited by negative memories retrieved from cues and how the effects of retrieval stopping are modulated by mental disorders, such as depression and anxiety. Participants engaged in retrieval stopping of aversive scene memories without any diversionary thoughts (...) (direct suppression, Experiment 1) or with diversionary positive thoughts (thought substitution, Experiment 2). Direct suppression reduced arousal elicited by the retrieval of aversive memories, while thought substitution did not only reduce arousal but also increased positive valence. Self-reported anxious/depressive symptoms negatively modulated the effects of direct suppression. For no or mild anxious/depressed individuals, direct suppression alleviated negative valence and high arousal when retrieving aversive memories. The negative relationship was not observed between the severity of the symptoms and the effect of thought substitution. These findings suggest that both retrieval stopping strategies can reduce distress from aversive memories. (shrink)

In this article, we propose the R2GQA system, a retriever-reader-generator question answering system, consisting of three main components: Document Retriever, Machine Reader, and Answer Generator. The Document Retriever module employs advanced information retrieval techniques to extract the context of articles from a dataset of legal regulation documents. The Machine Reader module utilizes state-of-the-art natural language understanding algorithms to comprehend the retrieved documents and extract answers. Finally, the Generator module synthesizes the extracted answers into concise and informative responses to questions (...) of students regarding legal regulations. Furthermore, we built the ViRHE4QA dataset in the domain of university training regulations, comprising 9,758 question-answer pairs with a rigorous construction process. This is the first Vietnamese dataset in the higher regulations domain with various types of answers, both extractive and abstractive. In addition, the R2GQA system is the first system to offer abstractive answers in Vietnamese. This paper discusses the design and implementation of each module within the R2GQA system on the ViRHE4QA dataset, highlighting their functionalities and interactions. Furthermore, we present experimental results demonstrating the effectiveness and utility of the proposed system in supporting the comprehension of students of legal regulations in higher education settings. In general, the R2GQA system and the ViRHE4QA dataset promise to contribute significantly to related research and help students navigate complex legal documents and regulations, empowering them to make informed decisions and adhere to institutional policies effectively. Our dataset is available for research purposes. (shrink)

Managing and retrieving complex legal information poses significant challenges, such as handling vast, interconnected legal texts efficiently and ensuring precise interpretation based on context, jurisdiction, and precedents. This paper addresses a critical gap in legal information retrieval systems by introducing an integrated knowledge graph-based approach combined with agent-driven decision making, with Tamil language support as an accessibility feature for regional users. Our comprehensive analysis of existing literature reveals that while current approaches address isolated aspects of the problem, none provides (...) a holistic dynamic solution. Our novel framework introduces two primary contributions: (1) a hierarchical knowledge graph schema that enables structured legal reasoning through multi-layered entity relationships, and (2) adaptive agent reasoning capabilities that dynamically navigate complex legal knowledge structures for contextually relevant responses. The system architecture integrates these core innovations with context-aware graph-based retrieval-augmented generation (RAG) for precise retrieval and incorporates Tamil language processing to enhance accessibility for regional users. Additionally, our approach demonstrates the significance of intent identification fine-tuning, which improves query understanding precision by 23% compared to baseline approaches. Experimental results demonstrate significant improvements in retrieval accuracy and query comprehension compared to traditional keyword-based approaches. Our hierarchical knowledge graph achieved a modularity score of 0.646 using Louvain community detection, while our LangGraph agent attained ROUGE-1/2/L scores of 0.467/0.311/0.508 for single-document tasks. For more complex multi-document retrieval, the ReAct Agent achieved impressive scores of 0.592/0.432/0.512, with high semantic similarity scores of 0.831 and 0.893. The proposed system offers a scalable solution that leverages structured knowledge representation and adaptive reasoning to navigate complex legal relationships, with Tamil language support providing enhanced accessibility for regional users in the Indian legal domain. (shrink)

This paper examines and critiques the ethical issues in postmortem sperm retrieval and the use of postmortem sperm to create new life. The article was occasioned by the recent request of the parents of a West Point cadet who died in a skiing accident at the Academy to retrieve and use his sperm to honor his memory and perpetuate the family name. The request occasioned national media attention. A trial court judge in New York in a two-page order authorized (...) both the retrieval and use of the postmortem sperm. (shrink)

The current understanding of the important role of information or knowledge retrieval to artificial intelligence is not adequate. In this article, three study areas related to retrieval are summarized and commented. The need for a more systematic and unified way of studying retrieval and intelligence is argued. A brief outline for future study is suggested, some philosophical and social implications of this study are also discussed.

This paper proposes an agent community based information retrieval method, which uses agent communities to manage and look up information related to users. An agent works as a delegate of its user and searches for information that the user wants by communicating with other agents. The communication between agents is carried out in a peer-to-peer computing architecture. In order to retrieve information related to a user query, an agent uses two histories : a query/retrieved document history(Q/RDH) and a query/sender (...) agent history(Q/SAH). The former is a list of pairs of a query and retrieved documents, where the queries were sent by the agent itself. The latter is a list of pairs of a query and sender agents and shows ``who sent what query to the agent''. This is useful to find a new information source. Making use of the Q/SAH is expected to cause a collaborative filtering effect, which gradually creates virtual agent communities, where agents with the same interests stay together. Our hypothesis is that a virtual agent community reduces communication loads to perform a search. As an agent receives more queries, then more links to new knowledge are achieved. From this behavior, a ``give and take''(or positive feedback) effect for agents seems to emerge. We implemented this method with Multi-Agents Kodama which has been developed in our laboratory, and conducted preliminary experiments to test the hypothesis. The empirical results showed that the method was much more efficient than a naive method employing 'broadcast' techniques only to look up a target agent. (shrink)

Research on action memory has been pursued for more than 30 years, but it is still unclear what drives the recollection process of performed actions. In this study, we used the remember/know paradigm and designed two experiments to examine the relation between item-specific processing and retrieval awareness of subject-performed tasks (SPT). The results showed that SPT allows remember responses in remember-know judgments more easily; that is, SPT can enhance the frequency of recalling re-collective experience. Item-specific processing can improve the (...) memory performance and the proportion of remember judgments of verbal tasks (VT), but it does not improve the memory performance and proportion of remember judgments of SPT, indicating that SPT can enhance item-specific processing, which leads to more remember responses in judgment. The relation between item-specific processing and retrieval awareness of SPT is also discussed. (shrink)

Long-term memory manages its contents to facilitate adaptive behaviour, amplifying representations of information relevant to current goals and expediting forgetting of information that competes with relevant memory traces. Both mnemonic selection and inhibition maintain congruence between the contents of long-term memory and an organism’s priorities. However, the capacity of these processes to modulate affective mnemonic representations remains ambiguous. Three empirical experiments investigated the consequences of mnemonic selection and inhibition on affectively charged and neutral mnemonic representations using an adapted retrieval (...) practice paradigm. Participants encoded neutral cue words and affectively negative or neutral associates and then selectively retrieved a subset of these associates multiple times. The consequences of selection and inhibitory processes engaged during selective retrieval were evaluated on a final memory test in which recall for all studied associates was probed. Analyses of memory recall indicated that both affectively neutral and negative mnemonic representations experienced similar levels of enhancement and impairment following selective retrieval, demonstrating the susceptibility of affectively salient memories to these mnemonic processes. These findings indicate that although affective memories may be more strongly encoded in memory, they remain amenable to inhibition and flexibly adaptable to the evolving needs of the organism. (shrink)

Emotional false memory findings using the DRM paradigm have been marked by higher false alarms to negatively arousing compared to neutral critical lure items. Explanations for these findings have mainly focused on false memory-based accounts. However, here we address the question of whether a response bias for emotional stimuli can, at least in part, explain this phenomenon. Participants viewed both neutral and negative arousing DRM lists and completed a recognition test in speeded or self-paced conditions. Speeded test reduces the opportunity (...) to adjust response bias. Analysis showed no significant difference in false recognition across critical lure types for the speeded condition, but false recognition was higher for negative compared to neutral critical lures in the self-paced condition. We argue that when retrieval does not allow for shifts in response criteria, false alarms to negative emotional critical lures appear more similar to neutral equivalents. The discussion explores memory-based and criterion-shift explanations for the enhanced emotional false memory finding. (shrink)

Large language models (LLMs) have captured the imagination of the public and the technical community. As powerful as they are they have problems that prohibit their use for highly skilled users. These issues are hallucinations, bias, black-box reasoning, and lack of domain depth. One of the most popular architectures to alleviate these problems is retrieval augmented generation (RAG). In a RAG architecture, the LLM is utilized to generate vectors and to parse and generate natural language. The knowledge base for (...) a RAG architecture is typically a set of documents focused on a particular type of vertical (question answering) or horizontal (domain) set of use cases as opposed to the general knowledge base of an LLM. Typically, the corpus for the RAG knowledge base is stored in a relational database. This project investigates the use of an ontology and knowledge graph to form a domain-specific knowledge base for RAG in order to leverage LLMs for specific domains without the four problems that typically make them inappropriate for mission and life critical domains. The domain is support of dental clinicians in India who face specific problems that can be significantly improved by better, timely, and easily accessible access to the latest knowledge on dental material products. We demonstrate that using an ontology and knowledge graph to implement RAG has several benefits such as rapid agile development and retrieval by reformulation browsing. (shrink)

Although it has been proposed that inhibition is related to individual differences in mathematical achievement, it is not clear how it is related to specific aspects of mathematical skills, such as arithmetic fact retrieval. The present study therefore investigated the association between inhibition and arithmetic fact retrieval and further examined the unique role of inhibition in individual differences in arithmetic fact retrieval, in addition to numerical magnitude processing. We administered measures of cognitive inhibition (i.e., numerical and non-numerical (...) stroop tasks) and a complementary, more ecologically valid measure of children’s inhibition in the classroom (i.e., teacher questionnaire), as well as numerical magnitude processing (i.e., symbolic and non-symbolic numerical magnitude comparison) and arithmetic fact retrieval (i.e., two verification tasks) in 86 typically developing third graders. We used a correlation, a regression and a Bayesian analysis. This study failed to observe a significant association between inhibition and arithmetic fact retrieval. Consequently, our results did not reveal a unique contribution of inhibition to arithmetic fact retrieval in addition to numerical magnitude processing. On the other hand, symbolic numerical magnitude processing turned out to be a very powerful predictor of arithmetic fact retrieval, as indicated by both frequentist and Bayesian approaches. (shrink)

The divine attributes of immensity and omnipresence have been integral to classical Christian confession regarding the nature of the triune God. Divine immensity and omnipresence are affirmed in doctrinal standards such as the Athanasian Creed (c. 500), the Fourth Lateran Council (1215), the Council of Basel (1431–49), the Second Helvetic Confession (1566), the Westminster Confession of Faith (1647), the Second London Baptist Confession (1689), and the First Vatican Council (1869–70). In the first section of this chapter, I offer a brief (...) historical overview of divine immensity and divine omnipresence in the Christian tradition. I then offer a brief taxonomy of contemporary models of divine omnipresence in the philosophical and theological landscape. In the second, more constructive section, I aim to gesture toward the retrieval of several classical insights regarding immensity and omnipresence that remain unexplored in contemporary analytic work. (shrink)