arXiv subjects

Ari Rappoport

Publications and source records attributed to Ari Rappoport.

At least 19 recordsLinked to original sources

arXiv2024

A CRH Theory of Autism Spectrum Disorder

This paper presents a complete theory of autism spectrum disorder (ASD), explaining its etiology, symptoms, and pathology. The core cause of ASD is excessive stress-induced postnatal release of corticotropin-releasing hormone (CRH). CRH competes with urocortins for binding to the CRH2 receptor, impairing their essential function in the utilization of glucose for growth. This results in impaired development of all brain areas depending on CRH2, including areas that are central in social development and eye gaze learning, and low-level sensory areas. Excessive CRH also induces excessive release of adrenal androgens (mainly DHEA), which impairs the long-term plasticity function of gonadal steroids. I show that these two effects can explain all of the known symptoms and properties of ASD. The theory is supported by strong diverse evidence, and points to very early detection biomarkers and preventive pharmaceutical treatments, one of which seems to be very promising.

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arXiv2024

A Dynorphin Theory of Depression and Bipolar Disorder

Major depressive disorder (MDD) is a debilitating health condition affecting a substantial part of the world's population. At present, there is no biological theory of MDD, and treatment is partial at best. Here I present a theory of MDD that explains its etiology, symptoms, pathophysiology, and treatment. MDD involves stressful life events that the person does not manage to resolve. In this situation animals normally execute a 'disengage' survival response. In MDD, this response is chronically executed, leading to depressed mood and the somatic MDD symptoms. To explain the biological mechanisms involved, I present a novel theory of opioids, where each opioid mediates one of the basic survival responses. The opioid mediating 'disengage' is dynorphin. The paper presents strong evidence for chronic dynorphin signaling in MDD and for its causal role in the disorder. The theory also explains bipolar disorder, and the mechanisms behind the treatment of both disorders.

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arXiv2024

A Sympathetic Nervous System Theory of Migraine

Migraine (MGR) ranks first among diseases in terms of years of lost healthy life in young adult and adult women. Currently, there is no theory of MGR. This paper presents a complete theory of migraine that explains its etiology, symptoms, pathology, and treatment. Migraine involves partially saturated (usually chronically high) sympathetic nervous system (SNS) activity, mainly due to higher sensitivity of the metabolic sensors that recruit it. MGR headache occurs when SNS activity is desensitized or excessive, resulting in hyperexcitability of baroreceptors, oxidative stress, and activation of pain pathways via TRPV1 channels and CGRP. The theory is supported by overwhelming evidence, and explains the properties of current MGR treatments.

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arXiv2024

A Polyunsaturated Fatty Acid (PUFA) Theory of Schizophrenia

I present a theory of schizophrenia (SZ) that mechanistically explains its etiology, symptoms, pathophysiology, and treatment. SZ involves the chronic release of membrane polyunsaturated fatty acids (PUFAs) and their utilization for the synthesis of stress-induced plasticity agents such as endocannabinoids (ECBs). The causal event in SZ is prolonged stress during a sensitive period, which can induce prolonged and heritable changes. The physiological effect of the released PUFAs and their products is to disconnect neurons from their inputs and promote intrinsic excitability. I show that these effects can explain the positive, negative, cognitive, and mood symptoms of SZ, as well as the mechanisms of many known triggers of psychosis. The theory is supported by overwhelming evidence addressing lipids, immunity, ECBs, neuromodulators, hormones, neurotransmitters, and cortical parameters in SZ. It explains why antipsychotic drugs are effective against positive symptoms, and why they do not affect the other symptoms. Finally, I present promising treatment directions implied by the theory, including some that are immediately available.

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arXiv2024

A CRH-HCN Theory of Obsessive-Compulsive Disorder (OCD)

I present the first complete theory of OCD. OCD occurs when excessive CRH is released in the prefrontal cortex, activating cAMP. cAMP is a major inducer of HCN channels, which promote repeated neural firing. The combination of CRH, which is strongly associated with stress, and repeated firing that cannot be controlled, explains all of the features of OCD, including obsessions and compulsions of all kinds.

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arXiv2023

A Lipid Rafts Theory of Alzheimer's Disease

I present a theory of Alzheimer's Disease (AD) that explains its symptoms, pathology, and risk factors. To do this, I introduce a new theory of brain plasticity that elucidates the physiological roles of AD-related agents. New events generate synaptic and branching candidates competing for long-term enhancement. Competition resolution crucially depends on the formation of membrane lipid rafts, which requires astrocyte-produced cholesterol. Sporadic AD is caused by impaired formation of plasma membrane lipid rafts, which prevents the conversion of short- to long-term memory, and yields excessive tau phosphorylation, intracellular cholesterol accumulation, synaptic dysfunction, and neurodegeneration. Amyloid beta (Abeta) production is promoted by cholesterol during the switch to competition resolution, and cholesterol accumulation stimulates chronic Abeta production, secretion, and aggregation. The theory addresses all of the major established facts known about the disease, and is supported by strong evidence.

q-bio.NC↗

arXiv2021

SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation

We announce a shared task on UCCA parsing in English, German and French, and call for participants to submit their systems. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general. Furthermore, existing applications for semantic evaluation that are based on UCCA will greatly benefit from better automatic methods for UCCA parsing. The competition website is https://competitions.codalab.org/competitions/19160

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arXiv2020

UCCA's Foundational Layer: Annotation Guidelines v2.1

This is the annotation manual for Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013), specifically the Foundational Layer. UCCA is a graph-based semantic annotation scheme based on typological linguistic principles. It has been applied to several languages; for ease of exposition these guidelines give examples mainly in English. New annotators may wish to start with the tutorial on the UCCA framework (Abend et al., 2020). Further resources are available at the project homepage: https://universalconceptualcognitiveannotation.github.io

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arXiv2020

SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA

We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. The shared task has yielded improvements over the state-of-the-art baseline in all languages and settings. Full results can be found in the task's website \url{https://competitions.codalab.org/competitions/19160}.

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arXiv2019

Content Differences in Syntactic and Semantic Representations

Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details of target schemes and constructions. We target this gap, and take Universal Dependencies (UD) and UCCA as a test case. After abstracting away from differences of convention or formalism, we find that most content divergences can be ascribed to: (1) UCCA's distinction between a Scene and a non-Scene; (2) UCCA's distinction between primary relations, secondary ones and participants; (3) different treatment of multi-word expressions, and (4) different treatment of inter-clause linkage. We further discuss the long tail of cases where the two schemes take markedly different approaches. Finally, we show that the proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. The substantial differences between the schemes suggest that semantic parsers are likely to benefit downstream text understanding applications beyond their syntactic counterparts.

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arXiv2018

BLEU is Not Suitable for the Evaluation of Text Simplification

BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments. We find low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved. Moreover, BLEU often negatively correlates with simplicity, essentially penalizing simpler sentences.

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arXiv2018

Semantic Structural Evaluation for Text Simplification

Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure to address structural aspects of text simplification, called SAMSA. It leverages recent advances in semantic parsing to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. SAMSA provides a reference-less automatic evaluation procedure, avoiding the problems that reference-based methods face due to the vast space of valid simplifications for a given sentence. Our human evaluation experiments show both SAMSA's substantial correlation with human judgments, as well as the deficiency of existing reference-based measures in evaluating structural simplification.

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arXiv2018

Simple and Effective Text Simplification Using Semantic and Neural Methods

Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.

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arXiv2018

Universal Dependency Parsing with a General Transition-Based DAG Parser

This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning. Our code is available at https://github.com/CoNLL-UD-2018/HUJI

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arXiv2018

Computing Word Classes Using Spectral Clustering

Clustering a lexicon of words is a well-studied problem in natural language processing (NLP). Word clusters are used to deal with sparse data in statistical language processing, as well as features for solving various NLP tasks (text categorization, question answering, named entity recognition and others). Spectral clustering is a widely used technique in the field of image processing and speech recognition. However, it has scarcely been explored in the context of NLP; specifically, the method used in this (Meila and Shi, 2001) has never been used to cluster a general word lexicon. We apply spectral clustering to a lexicon of words, evaluating the resulting clusters by using them as features for solving two classical NLP tasks: semantic role labeling and dependency parsing. We compare performance with Brown clustering, a widely-used technique for word clustering, as well as with other clustering methods. We show that spectral clusters produce similar results to Brown clusters, and outperform other clustering methods. In addition, we quantify the overlap between spectral and Brown clusters, showing that each model captures some information which is uncaptured by the other.

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arXiv2018

Multitask Parsing Across Semantic Representations

The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others. In this paper we tackle the challenging task of improving semantic parsing performance, taking UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD) parsing as auxiliary tasks. We experiment on three languages, using a uniform transition-based system and learning architecture for all parsing tasks. Despite notable conceptual, formal and domain differences, we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings.

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arXiv2017

Automatic Selection of Context Configurations for Improved Class-Specific Word Representations

This paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We introduce a simple yet effective framework for an automatic selection of class-specific context configurations. We construct a context configuration space based on universal dependency relations between words, and efficiently search this space with an adapted beam search algorithm. In word similarity tasks for each word class, we show that our framework is both effective and efficient. Particularly, it improves the Spearman's rho correlation with human scores on SimLex-999 over the best previously proposed class-specific contexts by 6 (A), 6 (V) and 5 (N) rho points. With our selected context configurations, we train on only 14% (A), 26.2% (V), and 33.6% (N) of all dependency-based contexts, resulting in a reduced training time. Our results generalise: we show that the configurations our algorithm learns for one English training setup outperform previously proposed context types in another training setup for English. Moreover, basing the configuration space on universal dependencies, it is possible to transfer the learned configurations to German and Italian. We also demonstrate improved per-class results over other context types in these two languages.

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arXiv2017

A Transition-Based Directed Acyclic Graph Parser for UCCA

We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.

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