Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms - fragmented across linguistics, psychology and computer science - by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Conformity-based walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.
In this work we extend previous analyses of linguistic networks by adopting a multi-layer network framework for modelling the human mental lexicon, i.e. an abstract mental repository where words and concepts are stored together with their linguistic patterns. Across a three-layer linguistic multiplex, we model English words as nodes and connect them according to (i) phonological similarities, (ii) synonym relationships and (iii) free word associations. Our main aim is to exploit this multi-layered structure to explore the influence of phonological and semantic relationships on lexicon assembly…
Institute of Engineering and Technology（工程技术学院）；Indian Institute of Technology（印度理工学院）；Nanyang Technological University（南洋理工大学）
In the past few years, social media has risen as a platform where people express and share personal incidences about abuse, violence and mental health issues. There is a need to pinpoint such posts and learn the kind of response expected. For this purpose, we understand the sentiment that a personal story elicits on different posts present on different social media sites, on the topics of abuse or mental health. In this paper, we propose a method supported by hand-crafted features to judge if the post requires an empathetic response. The model is trained upon posts from various web-pages and c…
Institute for Complex Systems Simulation, University of Southampton（南安普顿大学复杂系统模拟研究所）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；Department of Electrical Engineering and Computer Science, University of Kansas（堪萨斯大学电气工程与计算机科学系）；School of Computer Science and Mathematics, Universitat Rovira i Virgili（罗维拉-威尔吉利大学计算机科学与数学学院）
Word similarities affect language acquisition and use in a multi-relational way barely accounted for in the literature. We propose a multiplex network representation of this mental lexicon of word similarities as a natural framework for investigating large-scale cognitive patterns. Our representation accounts for semantic, taxonomic, and phonological interactions and it identifies a cluster of words which are used with greater frequency, are identified, memorised, and learned more easily, and have more meanings than expected at random. This cluster emerges around age 7 through an explosive tra…
Multi-Level Narrative Evaluation Outperforms Lexical Features for Mental Health
Institute for Artificial Intelligence（人工智能研究院）；School of Psychological and Cognitive Sciences（心理学与认知科学学院）；School of Intelligence Science and Technology（智能科学与技术学院）；State Key Laboratory of General AI（通用人工智能国家重点实验室）；Beijing Key Laboratory of Behavior and Mental Health（北京行为与心理健康重点实验室）；PKU-Changsha Institute for Computing and Digital Economy（北京大学长沙计算与数字经济研究院）
University of Southampton（南安普顿大学）；University of Kansas（堪萨斯大学）
Network models of language have provided a way of linking cognitive processes to the structure and connectivity of language. However, one shortcoming of current approaches is focusing on only one type of linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N=529 words/nodes are connected according to four types of relationships: (i) free associations, (ii) feature sharing, (iii) co-occurr…
We introduce the bilingual dual-coding theory as a model for bilingual mental representation. Based on this model, lexical selection neural networks are implemented for a connectionist transfer project in machine translation. This lexical selection approach has two advantages. First, it is learnable. Little human effort on knowledge engineering is required. Secondly, it is psycholinguistically well-founded.
University College Dublin（都柏林大学学院）；Swansea University（斯旺西大学）
Through proliferation on smartphones and smart speakers, intelligent personal assistants (IPAs) have made speech a common interaction modality. Yet, due to linguistic coverage and varying levels of functionality, many speakers engage with IPAs using a non-native language. This may impact the mental workload and pattern of language production displayed by non-native speakers. We present a mixed-design experiment, wherein native (L1) and non-native (L2) English speakers completed tasks with IPAs through smartphones and smart speakers. We found significantly higher mental workload for L2 speakers…
Recent work has shown that distributed word representations can encode abstract information from child-directed speech. In this paper, we use diachronic distributed word representations to perform temporal modeling and analysis of lexical development in children. Unlike all previous work, we use temporally sliced corpus to learn distributed word representations of child-speech and child-directed speech under a curriculum-learning setting. In our experiments, we perform a lexical categorization task to plot the semantic and syntactic knowledge acquisition trajectories in children. Next, we perf…
SpreadPy: A Python tool for modelling spreading activation and superdiffusion in cognitive multiplex networks
CogNosco Lab, Department of Psychology and Cognitive Science, University of Trento（CogNosco实验室，心理学与认知科学系，特伦托大学）；Department of Psychology, National University of Singapore（心理学系，新加坡国立大学）
RWTH Aachen University（亚琛工业大学）；University of Amsterdam（阿姆斯特丹大学）
In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing and machine learning techniques. While significant progress has been achieved in this interdisciplinary research area, the vast majority of work has treated MHD as a binary classification task. The multiclass classification setup is, however, essential if we are to uncover the subtle differences among the statistical patterns of language use associated with particular mental health conditions. Here, we report on exp…
Large Language Models for Mental Health: A Multilingual Evaluation
George Mason University（乔治·马歇尔大学）；Interdisciplinary School of Doctoral Studies, University of Bucharest（布加勒斯特大学跨学科博士研究学院）；PRHLT Research Center, Universitat Politècnica de València（瓦伦西亚理工大学PRHLT研究中心）；University of Minnesota（明尼苏达大学）
Most words are ambiguous--i.e., they convey distinct meanings in different contexts--and even the meanings of unambiguous words are context-dependent. Both phenomena present a challenge for NLP. Recently, the advent of contextualized word embeddings has led to success on tasks involving lexical ambiguity, such as Word Sense Disambiguation. However, there are few tasks that directly evaluate how well these contextualized embeddings accommodate the more continuous, dynamic nature of word meaning--particularly in a way that matches human intuitions. We introduce RAW-C, a dataset of graded, human …
Reading is a complex process which requires proper understanding of texts in order to create coherent mental representations. However, comprehension problems may arise due to hard-to-understand sections, which can prove troublesome for readers, while accounting for their specific language skills. As such, steps towards simplifying these sections can be performed, by accurately identifying and evaluating difficult structures. In this paper, we describe our approach for the SemEval-2021 Task 1: Lexical Complexity Prediction competition that consists of a mixture of advanced NLP techniques, namel…
LIMSI-CNRS（LIMSI-CNRS法国国家科学研究中心）；Université Paris XI（巴黎第十一大学）
The study of the Tip of the Tongue phenomenon (TOT) provides valuable clues and insights concerning the organisation of the mental lexicon (meaning, number of syllables, relation with other words, etc.). This paper describes a tool based on psycho-linguistic observations concerning the TOT phenomenon. We've built it to enable a speaker/writer to find the word he is looking for, word he may know, but which he is unable to access in time. We try to simulate the TOT phenomenon by creating a situation where the system knows the target word, yet is unable to access it. In order to find the target w…
Alzheimer's Disease (AD) is nowadays the most common form of dementia, and its automatic detection can help to identify symptoms at early stages, so that preventive actions can be carried out. Moreover, non-intrusive techniques based on spoken data are crucial for the development of AD automatic detection systems. In this light, this paper is presented as a contribution to the ADReSS Challenge, aiming at improving AD automatic detection from spontaneous speech. To this end, recordings from 108 participants, which are age-, gender-, and AD condition-balanced, have been used as training set to p…
Commonwealth Scientific and Industrial Research Organization（澳大利亚联邦科学与工业研究组织）；Australian National University（澳大利亚国立大学）；Australasian Institute of Digital Health（澳大拉西亚数字健康研究所）
Background: Assisting moderators to triage harmful posts in Internet Support Groups is relevant to ensure its safe use. Automated text classification methods analysing the language expressed in posts of online forums is a promising solution. Methods: Natural Language Processing and Machine Learning technologies were used to build a triage post classifier using a dataset from Reachout mental health forum for young people. Results: When comparing with the state-of-the-art, a solution mainly based on features from lexical resources, received the best classification performance for the crisis post…
SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support
Zhejiang University（浙江大学）；School of Engineering, Westlake University（西lake大学工程学院）；Research Center for Industries of the Future, Westlake University（西lake大学未来产业研究中心）
Auditing Framing-Sensitive Behavioral Instability in Large Language Models for Mental Health Interactions
a School of computer science, digital engineering and AI, Long Island University, Brooklyn, NY, USA（a 计算科学与数字工程及人工智能学院，罗格斯大学，布鲁克林，纽约，美国）；b Department of Psychology, Long Island University, Brooklyn, NY, USA（b 心理学系，罗格斯大学，布鲁克林，纽约，美国）
The utility of Twitter data as a medium to support population-level mental health monitoring is not well understood. In an effort to better understand the predictive power of supervised machine learning classifiers and the influence of feature sets for efficiently classifying depression-related tweets on a large-scale, we conducted two feature study experiments. In the first experiment, we assessed the contribution of feature groups such as lexical information (e.g., unigrams) and emotions (e.g., strongly negative) using a feature ablation study. In the second experiment, we determined the per…
An experimental and computational study of an Estonian single-person word naming
University of Tartu（塔尔图大学）；Institute of Estonian Language（爱沙尼亚语言研究所）；University of Tübingen（图宾根大学）；Institute of Estonian and General Linguistics（爱沙尼亚与一般语言研究所）
University of Exeter（埃克塞特大学）；University of Pisa（比萨大学）；Institute of Information Science and Technologies, National Research Council（国家研究委员会信息科学与技术研究所）；University of Ghent（根特大学）；Technion, Israel Institute of Technology（以色列理工学院）；University of Kansas（堪萨斯大学）
The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? We here review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating t…
Harvard University（哈佛大学）；McGovern Institute for Brain Research, Massachusetts Institute of Technology（麻省理工学院麦戈文脑研究所）；Siemens Healthineers（西门子医疗）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
The words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals. However, language can represent only a subset of our rich and detailed cognitive architecture. Here, we ask what kinds of common knowledge (semantic memory) are captured by word meanings (lexical semantics). We examine a prominent computational model that represents words as vectors in a multidimensional space, such that proximity between word-vectors approximates semantic relatedness. Because related words appear in similar contexts, such spaces - called "word embeddings" - c…
University of Wuppertal（伍珀塔尔大学）；Universität Hamburg（汉堡大学）；International Psychoanalytic University（国际精神分析大学）；University of Exeter（埃克塞特大学）；The University of Sydney（悉尼大学）；McGill University（麦吉尔大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Indiana University（印第安纳大学）
Though there is a strong consensus that word length and frequency are the most important single-word features determining visual-orthographic access to the mental lexicon, there is less agreement as how to best capture syntactic and semantic factors. The traditional approach in cognitive reading research assumes that word predictability from sentence context is best captured by cloze completion probability (CCP) derived from human performance data. We review recent research suggesting that probabilistic language models provide deeper explanations for syntactic and semantic effects than CCP. Th…
Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London（伦敦大学学院惠康人类神经成像中心、神经病学研究所）
A theory of language learning is described, which uses Bayesian induction of feature structures (scripts) and script functions. Each word sense in a language is mentally represented by an m-script, a script function which embodies all the syntax and semantics of the word. M-scripts form a fully-lexicalised unification grammar, which can support adult language. Each word m-script can be learnt robustly from about six learning examples. The theory has been implemented as a computer model, which can bootstrap-learn a language from zero vocabulary. The Bayesian learning mechanism is (1) Capable: t…
