Paper Group NANR 67
Reviving a psychometric measure: Classification and prediction of the Operant Motive Test. Coherence models in schizophrenia. Robust Variational Bayesian Point Set Registration. Meta-Semantic Representation for Early Detection of Alzheimer’s Disease. Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning. P …
Reviving a psychometric measure: Classification and prediction of the Operant Motive Test
| Title | Reviving a psychometric measure: Classification and prediction of the Operant Motive Test |
| Authors | Dirk Johann{\ss}en, Chris Biemann, David Scheffer |
| Abstract | Implicit motives allow for the characterization of behavior, subsequent success and long-term development. While this has been operationalized in the operant motive test, research on motives has declined mainly due to labor-intensive and costly human annotation. In this study, we analyze over 200,000 labeled data items from 40,000 participants and utilize them for engineering features for training a logistic model tree machine learning model. It captures manually assigned motives well with an F-score of 80{%}, coming close to the pairwise annotator intraclass correlation coefficient of r = .85. In addition, we found a significant correlation of r = .2 between subsequent academic success and data automatically labeled with our model in an extrinsic evaluation. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-3014/ |
| https://www.aclweb.org/anthology/W19-3014 | |
| PWC | https://paperswithcode.com/paper/reviving-a-psychometric-measure |
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Coherence models in schizophrenia
| Title | Coherence models in schizophrenia |
| Authors | S Just, ra, Erik Haegert, Nora Ko{\v{r}}{'a}nov{'a}, Anna-Lena Br{"o}cker, Ivan Nenchev, Jakob Funcke, Christiane Montag, Manfred Stede |
| Abstract | Incoherent discourse in schizophrenia has long been recognized as a dominant symptom of the mental disorder (Bleuler, 1911/1950). Recent studies have used modern sentence and word embeddings to compute coherence metrics for spontaneous speech in schizophrenia. While clinical ratings always have a subjective element, computational linguistic methodology allows quantification of speech abnormalities. Clinical and empirical knowledge from psychiatry provide the theoretical and conceptual basis for modelling. Our study is an interdisciplinary attempt at improving coherence models in schizophrenia. Speech samples were obtained from healthy controls and patients with a diagnosis of schizophrenia or schizoaffective disorder and different severity of positive formal thought disorder. Interviews were transcribed and coherence metrics derived from different embeddings. One model found higher coherence metrics for controls than patients. All other models remained non-significant. More detailed analysis of the data motivates different approaches to improving coherence models in schizophrenia, e.g. by assessing referential abnormalities. |
| Tasks | Word Embeddings |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-3015/ |
| https://www.aclweb.org/anthology/W19-3015 | |
| PWC | https://paperswithcode.com/paper/coherence-models-in-schizophrenia |
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Robust Variational Bayesian Point Set Registration
| Title | Robust Variational Bayesian Point Set Registration |
| Authors | Jie Zhou, Xinke Ma, Li Liang, Yang Yang, Shijin Xu, Yuhe Liu, Sim-Heng Ong |
| Abstract | In this work, we propose a hierarchical Bayesian network based point set registration method to solve missing correspondences and various massive outliers. We construct this network first using the finite Student s t latent mixture model (TLMM), in which distributions of latent variables are estimated by a tree-structured variational inference (VI) so that to obtain a tighter lower bound under the Bayesian framework. We then divide the TLMM into two different mixtures with isotropic and anisotropic covariances for correspondences recovering and outliers identification, respectively. Finally, the parameters of mixing proportion and covariances are both taken as latent variables, which benefits explaining of missing correspondences and heteroscedastic outliers. In addition, a cooling schedule is adopted to anneal prior on covariances and scale variables within designed two phases of transformation, it anneal priors on global and local variables to perform a coarse-to- fine registration. In experiments, our method outperforms five state-of-the-art methods in synthetic point set and realistic imaging registrations. |
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| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Robust_Variational_Bayesian_Point_Set_Registration_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Robust_Variational_Bayesian_Point_Set_Registration_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/robust-variational-bayesian-point-set |
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Meta-Semantic Representation for Early Detection of Alzheimer’s Disease
| Title | Meta-Semantic Representation for Early Detection of Alzheimer’s Disease |
| Authors | Jinho D. Choi, Mengmei Li, Felicia Goldstein, Ihab Hajjar |
| Abstract | This paper presents a new task-oriented meaning representation called meta-semantics, that is designed to detect patients with early symptoms of Alzheimer{'}s disease by analyzing their language beyond a syntactic or semantic level. Meta-semantic representation consists of three parts, entities, predicate argument structures, and discourse attributes, that derive rich knowledge graphs. For this study, 50 controls and 50 patients with mild cognitive impairment (MCI) are selected, and meta-semantic representation is annotated on their speeches transcribed in text. Inter-annotator agreement scores of 88{%}, 82{%}, and 89{%} are achieved for the three types of annotation, respectively. Five analyses are made using this annotation, depicting clear distinctions between the control and MCI groups. Finally, a neural model is trained on features extracted from those analyses to classify MCI patients from normal controls, showing a high accuracy of 82{%} that is very promising. |
| Tasks | Knowledge Graphs |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-3309/ |
| https://www.aclweb.org/anthology/W19-3309 | |
| PWC | https://paperswithcode.com/paper/meta-semantic-representation-for-early |
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Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning
| Title | Improving Composition of Sentence Embeddings through the Lens of Statistical Relational Learning |
| Authors | Damien Sileo, Tim Van de Cruys, Camille Pradel, Philippe Muller |
| Abstract | Various NLP problems – such as the prediction of sentence similarity, entailment, and discourse relations – are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. We call them textual relational problems. A popular model for textual relational problems is to embed sentences into fixed size vectors and use composition functions (e.g. difference or concatenation) of those vectors as features for the prediction. Meanwhile, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). In this work, we show that textual relational models implicitly use compositions from baseline SRL models. We show that such compositions are not expressive enough for several tasks (e.g. natural language inference). We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction. |
| Tasks | Natural Language Inference, Relational Reasoning, Representation Learning, Sentence Embeddings |
| Published | 2019-05-01 |
| URL | https://openreview.net/forum?id=SkxZFoAqtQ |
| https://openreview.net/pdf?id=SkxZFoAqtQ | |
| PWC | https://paperswithcode.com/paper/improving-composition-of-sentence-embeddings |
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Predicting Suicide Risk from Online Postings in Reddit The UGent-IDLab submission to the CLPysch 2019 Shared Task A
| Title | Predicting Suicide Risk from Online Postings in Reddit The UGent-IDLab submission to the CLPysch 2019 Shared Task A |
| Authors | Semere Kiros Bitew, Giannis Bekoulis, Johannes Deleu, Lucas Sterckx, Klim Zaporojets, Thomas Demeester, Chris Develder |
| Abstract | This paper describes IDLab{'}s text classification systems submitted to Task A as part of the CLPsych 2019 shared task. The aim of this shared task was to develop automated systems that predict the degree of suicide risk of people based on their posts on Reddit. Bag-of-words features, emotion features and post level predictions are used to derive user-level predictions. Linear models and ensembles of these models are used to predict final scores. We find that predicting fine-grained risk levels is much more difficult than flagging potentially at-risk users. Furthermore, we do not find clear added value from building richer ensembles compared to simple baselines, given the available training data and the nature of the prediction task. |
| Tasks | Text Classification |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-3019/ |
| https://www.aclweb.org/anthology/W19-3019 | |
| PWC | https://paperswithcode.com/paper/predicting-suicide-risk-from-online-postings |
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Robustness and Equivariance of Neural Networks
| Title | Robustness and Equivariance of Neural Networks |
| Authors | Amit Deshpande, Sandesh Kamath, K.V.Subrahmanyam |
| Abstract | Neural networks models are known to be vulnerable to geometric transformations as well as small pixel-wise perturbations of input. Convolutional Neural Networks (CNNs) are translation-equivariant but can be easily fooled using rotations and small pixel-wise perturbations. Moreover, CNNs require sufficient translations in their training data to achieve translation-invariance. Recent work by Cohen & Welling (2016), Worrall et al. (2016), Kondor & Trivedi (2018), Cohen & Welling (2017), Marcos et al. (2017), and Esteves et al. (2018) has gone beyond translations, and constructed rotation-equivariant or more general group-equivariant neural network models. In this paper, we do an extensive empirical study of various rotation-equivariant neural network models to understand how effectively they learn rotations. This includes Group-equivariant Convolutional Networks (GCNNs) by Cohen & Welling (2016), Harmonic Networks (H-Nets) by Worrall et al. (2016), Polar Transformer Networks (PTN) by Esteves et al. (2018) and Rotation equivariant vector field networks by Marcos et al. (2017). We empirically compare the ability of these networks to learn rotations efficiently in terms of their number of parameters, sample complexity, rotation augmentation used in training. We compare them against each other as well as Standard CNNs. We observe that as these rotation-equivariant neural networks learn rotations, they instead become more vulnerable to small pixel-wise adversarial attacks, e.g., Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD), in comparison with Standard CNNs. In other words, robustness to geometric transformations in these models comes at the cost of robustness to small pixel-wise perturbations. |
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| Published | 2019-05-01 |
| URL | https://openreview.net/forum?id=Ske25sC9FQ |
| https://openreview.net/pdf?id=Ske25sC9FQ | |
| PWC | https://paperswithcode.com/paper/robustness-and-equivariance-of-neural |
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Modelling Uncertainty in Collaborative Document Quality Assessment
| Title | Modelling Uncertainty in Collaborative Document Quality Assessment |
| Authors | Aili Shen, Daniel Beck, Bahar Salehi, Jianzhong Qi, Timothy Baldwin |
| Abstract | In the context of document quality assessment, previous work has mainly focused on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task. To imitate people{'}s disagreement over inherently subjective tasks such as rating the quality of a Wikipedia article, a document quality assessment system should provide not only a prediction of the article quality but also the uncertainty over its predictions. This motivates us to measure the uncertainty in document quality predictions, in addition to making the label prediction. Experimental results show that both Gaussian processes (GPs) and random forests (RFs) can yield competitive results in predicting the quality of Wikipedia articles, while providing an estimate of uncertainty when there is inconsistency in the quality labels from the Wikipedia contributors. We additionally evaluate our methods in the context of a semi-automated document quality class assignment decision-making process, where there is asymmetric risk associated with overestimates and underestimates of document quality. Our experiments suggest that GPs provide more reliable estimates in this context. |
| Tasks | Decision Making, Gaussian Processes |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5525/ |
| https://www.aclweb.org/anthology/D19-5525 | |
| PWC | https://paperswithcode.com/paper/modelling-uncertainty-in-collaborative |
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Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task
| Title | Suicide Risk Assessment on Social Media: USI-UPF at the CLPsych 2019 Shared Task |
| Authors | Esteban R{'\i}ssola, Diana Ram{'\i}rez-Cifuentes, Ana Freire, Fabio Crestani |
| Abstract | This paper describes the participation of the USI-UPF team at the shared task of the 2019 Computational Linguistics and Clinical Psychology Workshop (CLPsych2019). The goal is to assess the degree of suicide risk of social media users given a labelled dataset with their posts. An appropriate suicide risk assessment, with the usage of automated methods, can assist experts on the detection of people at risk and eventually contribute to prevent suicide. We propose a set of machine learning models with features based on lexicons, word embeddings, word level n-grams, and statistics extracted from users{'} posts. The results show that the most effective models for the tasks are obtained integrating lexicon-based features, a selected set of n-grams, and statistical measures. |
| Tasks | Word Embeddings |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-3021/ |
| https://www.aclweb.org/anthology/W19-3021 | |
| PWC | https://paperswithcode.com/paper/suicide-risk-assessment-on-social-media-usi |
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A Survey of Recent Advances in Efficient Parsing for Graph Grammars
| Title | A Survey of Recent Advances in Efficient Parsing for Graph Grammars |
| Authors | Frank Drewes |
| Abstract | |
| Tasks | |
| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/W19-3102/ |
| https://www.aclweb.org/anthology/W19-3102 | |
| PWC | https://paperswithcode.com/paper/a-survey-of-recent-advances-in-efficient |
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Latent Variable Grammars for Discontinuous Parsing
| Title | Latent Variable Grammars for Discontinuous Parsing |
| Authors | Kilian Gebhardt |
| Abstract | |
| Tasks | |
| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/W19-3103/ |
| https://www.aclweb.org/anthology/W19-3103 | |
| PWC | https://paperswithcode.com/paper/latent-variable-grammars-for-discontinuous |
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Composition of Sentence Embeddings: Lessons from Statistical Relational Learning
| Title | Composition of Sentence Embeddings: Lessons from Statistical Relational Learning |
| Authors | Damien Sileo, Tim Van De Cruys, Camille Pradel, Philippe Muller |
| Abstract | Various NLP problems {–} such as the prediction of sentence similarity, entailment, and discourse relations {–} are all instances of the same general task: the modeling of semantic relations between a pair of textual elements. A popular model for such problems is to embed sentences into fixed size vectors, and use composition functions (e.g. concatenation or sum) of those vectors as features for the prediction. At the same time, composition of embeddings has been a main focus within the field of Statistical Relational Learning (SRL) whose goal is to predict relations between entities (typically from knowledge base triples). In this article, we show that previous work on relation prediction between texts implicitly uses compositions from baseline SRL models. We show that such compositions are not expressive enough for several tasks (e.g. natural language inference). We build on recent SRL models to address textual relational problems, showing that they are more expressive, and can alleviate issues from simpler compositions. The resulting models significantly improve the state of the art in both transferable sentence representation learning and relation prediction. |
| Tasks | Natural Language Inference, Relational Reasoning, Representation Learning, Sentence Embeddings |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-1004/ |
| https://www.aclweb.org/anthology/S19-1004 | |
| PWC | https://paperswithcode.com/paper/composition-of-embeddings-lessons-from |
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Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs
| Title | Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs |
| Authors | Johanna Bj{"o}rklund, Shay B. Cohen, Frank Drewes, Giorgio Satta |
| Abstract | We propose a formal model for translating unranked syntactic trees, such as dependency trees, into semantic graphs. These tree-to-graph transducers can serve as a formal basis of transition systems for semantic parsing which recently have been shown to perform very well, yet hitherto lack formalization. Our model features {``}extended{''} rules and an arc-factored normal form, comes with an efficient translation algorithm, and can be equipped with weights in a straightforward manner. | |
| Tasks | Semantic Parsing |
| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/W19-3104/ |
| https://www.aclweb.org/anthology/W19-3104 | |
| PWC | https://paperswithcode.com/paper/bottom-up-unranked-tree-to-graph-transducers |
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TOI-CNN: a Solution of Information Extraction on Chinese Insurance Policy
| Title | TOI-CNN: a Solution of Information Extraction on Chinese Insurance Policy |
| Authors | Lin Sun, Kai Zhang, Fule Ji, Zhenhua Yang |
| Abstract | Contract analysis can significantly ease the work for humans using AI techniques. This paper shows a problem of Element Tagging on Insurance Policy (ETIP). A novel Text-Of-Interest Convolutional Neural Network (TOI-CNN) is proposed for the ETIP solution. We introduce a TOI pooling layer to replace traditional pooling layer for processing the nested phrasal or clausal elements in insurance policies. The advantage of TOI pooling layer is that the nested elements from one sentence could share computation and context in the forward and backward passes. The computation of backpropagation through TOI pooling is also demonstrated in the paper. We have collected a large Chinese insurance contract dataset and labeled the critical elements of seven categories to test the performance of the proposed method. The results show the promising performance of our method in the ETIP problem. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/N19-2022/ |
| https://www.aclweb.org/anthology/N19-2022 | |
| PWC | https://paperswithcode.com/paper/toi-cnn-a-solution-of-information-extraction |
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On the Compression of Lexicon Transducers
| Title | On the Compression of Lexicon Transducers |
| Authors | Marco Cognetta, Cyril Allauzen, Michael Riley |
| Abstract | In finite-state language processing pipelines, a lexicon is often a key component. It needs to be comprehensive to ensure accuracy, reducing out-of-vocabulary misses. However, in memory-constrained environments (e.g., mobile phones), the size of the component automata must be kept small. Indeed, a delicate balance between comprehensiveness, speed, and memory must be struck to conform to device requirements while providing a good user experience.In this paper, we describe a compression scheme for lexicons when represented as finite-state transducers. We efficiently encode the graph of the transducer while storing transition labels separately. The graph encoding scheme is based on the LOUDS (Level Order Unary Degree Sequence) tree representation, which has constant time tree traversal for queries while being information-theoretically optimal in space. We find that our encoding is near the theoretical lower bound for such graphs and substantially outperforms more traditional representations in space while remaining competitive in latency benchmarks. |
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| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/W19-3105/ |
| https://www.aclweb.org/anthology/W19-3105 | |
| PWC | https://paperswithcode.com/paper/on-the-compression-of-lexicon-transducers |
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