Word Sense Disambiguation: A Structured Learning Perspective

This paper explores the application of structured learning methods (SLMs) to word sense disambiguation (WSD). On one hand, the semantic dependencies between polysemous words in the sentence can be encoded in SLMs. On the other hand, SLMs obtained significant achievements in natural language processing, and so it is a natural idea to apply them to WSD. However, there are many theoretical and practical problems when SLMs are applied to WSD, due to characteristics of WSD. Beginning with the method based on hidden Markov model, this paper proposes for the first time a comprehensive and unified solution for WSD based on maximum entropy Markov model, conditional random field and tree-structured conditional random field, and reduces the time complexity and running time of the proposed methods to a reasonable level by beam search, approximate training, and parallel training. The update of models brings performance improvement, the introduction of one step dependency improves performance by 1--5 percent, the adoption of non-independent features improves performance by 2--3 percent, and the extension of underlying structure to dependency parsing tree improves performance by about 1 percent. On the English all-words WSD dataset of Senseval-2004, the method based on tree-structured conditional random field outperforms the best attendee system significantly. Nevertheless, almost all machine learning methods suffer from data sparseness due to the scarcity of sense tagged data, and so do SLMs. Besides improving structured learning methods according to the characteristics of WSD, another approach to improve disambiguation performance is to mine disambiguation knowledge from all kinds of sources, such as Wikipedia, parallel corpus, and to alleviate knowledge acquisition bottleneck of WSD

Synchronization in the quaternionic Kuramoto model

In this paper, we propose an $N$ oscillators Kuramoto model with quaternions $\mathbb{H}$. In case the coupling strength is strong, a sufficient condition of synchronization is established for general $N\geqslant 2$. On the other hand, we analyze the case when the coupling strength is weak. For $N=2$, when coupling strength is weak (below the critical coupling strength $\lambda_c$), we show that new periodic orbits emerge near each equilibrium point, and hence phase-locking state exists. This phenomenon is different from the real Kuramoto system since it is impossible to arrive at any synchronization when $\lambda<\lambda_c$. A theorem is proved which states that the closed contours form a set of "Baumkuchen" that is dense near each equilibrium point. In other words, the trajectory of phase difference lies on a $4D$-torus surface. Therefore, this implies that the phase-locking state is Lyapunov stable but not asymptotically stable. The proof uses a new infinite buffer method ("$\delta/n$ criterion") and a Lyapunov function argument. This has been studied both analytically and numerically. For $N=3$, we consider Lion Dance flow, the analog of Cherry flow, to demonstrate that the quaternionic synchronization exists even when the coupling strength is "super weak" (when $\lambda/\omega 3$, the stable manifold of Lion Dance flow exists, and the number of these equilibria is $\lfloor \frac{N-1}{2}\rfloor$. Therefore, we conjecture that quaternionic synchronization always exists.Comment: 35 pages, 6 figure

Global Existence and Stability to the Isothermal Gas Dynamics System with an Outer Force

En este artículo aplicamos el método clásico de viscosidad, junto con la aproximación de flujo y la teoría de la compacidad compensada, para obtener la existencia global de las soluciones entrópicas acotadas para el sistema dinámico de gas isotérmico con una fuente externa. Las estimaciones a priori de L∞ independientes del tiempo se prueban aplicando el principio máximo para un sistema parabólico acoplado no lineal adecuado de dos ecuaciones.In this paper, we apply the classical viscosity method, coupled with the flux approximation and the compensated compactness theory to obtain the global existence of the bounded entropy solutions for the isothermal gas dynamics system with an outer source. The a-priori time-independent L∞ estimates are proved by applying the maximum principle to a suitable nonlinear coupled parabolic system of two equations

1,4-Bis(thio­phen-2-yl)butane-1,4-dione

In the centrosymmetric title compound, C12H10O2S2, the alkyl chains adopt a fully extended all-trans conformation with respect to the C(thio­phene)—C bond. The non-H atoms of the mol­ecule are nearly planar, with a maximum deviation of 0.063 (2) Å from the mean plane of the constituent atoms. In the crystal, symmetry-related mol­ecules are linked via pairs of C—H⋯π contacts [H–centroid distances of the thio­phene units = 2.79 (9) and 2.82 (4) Å], in turn inter­digitating with each other along the bc plane, thus leading to an inter­woven two-dimensional network

Global Gene Knockout of Kcnip3 Enhances Pain Sensitivity and Exacerbates Negative Emotions in Rats

The Ca2+-binding protein Kv channel interacting protein 3 (KChIP3) or downstream regulatory element antagonist modulator (DREAM), a member of the neuronal calcium sensor (NCS) family, shows remarkable multifunctional properties. It acts as a transcriptional repressor in the nucleus and a modulator of ion channels or receptors, such as Kv4, NMDA receptors and TRPV1 channels on the cytomembrane. Previous studies of Kcnip3-/- mice have indicated that KChIP3 facilitates pain hypersensitivity by repressing Pdyn expression in the spinal cord. Conversely, studies from transgenic daDREAM (dominant active DREAM) mice indicated that KChIP3 contributes to analgesia by repressing Bdnf expression and attenuating the development of central sensitization. To further determine the role of KChIP3 in pain transmission and its possible involvement in emotional processing, we assessed the pain sensitivity and negative emotional behaviors of Kcnip3-/- rats. The knockout rats showed higher pain sensitivity compared to the wild-type rats both in the acute nociceptive pain model and in the late phase (i.e., 2, 4 and 6 days post complete Freund’s adjuvant injection) of the chronic inflammatory pain model. Importantly, Kcnip3-/- rats displayed stronger aversion to the pain-associated compartment, higher anxiety level and aggravated depression-like behavior. Furthermore, RNA-Seq transcriptional profiling of the forebrain cortex were compared between wild-type and Kcnip3-/- rats. Among the 68 upregulated genes, 19 genes (including Nr4a2, Ret, Cplx3, Rgs9, and Itgad) are associated with neural development or synaptic transmission, particularly dopamine neurotransmission. Among the 79 downregulated genes, 16 genes (including Col3a1, Itm2a, Pcdhb3, Pcdhb22, Pcdhb20, Ddc, and Sncaip) are associated with neural development or dopaminergic transmission. Transcriptional upregulation of Nr4a2, Ret, Cplx3 and Rgs9, and downregulation of Col3a1, Itm2a, Pcdhb3 and Ddc, were validated by qPCR analysis. In summary, our studies showed that Kcnip3-/- rats displayed higher pain sensitivity and stronger negative emotions, suggesting an involvement of KChIP3 in negative emotions and possible role in central nociceptive processing

NYCU-TWO at Memotion 3: Good Foundation, Good Teacher, then you have Good Meme Analysis

This paper presents a robust solution to the Memotion 3.0 Shared Task. The goal of this task is to classify the emotion and the corresponding intensity expressed by memes, which are usually in the form of images with short captions on social media. Understanding the multi-modal features of the given memes will be the key to solving the task. In this work, we use CLIP to extract aligned image-text features and propose a novel meme sentiment analysis framework, consisting of a Cooperative Teaching Model (CTM) for Task A and a Cascaded Emotion Classifier (CEC) for Tasks B&C. CTM is based on the idea of knowledge distillation, and can better predict the sentiment of a given meme in Task A; CEC can leverage the emotion intensity suggestion from the prediction of Task C to classify the emotion more precisely in Task B. Experiments show that we achieved the 2nd place ranking for both Task A and Task B and the 4th place ranking for Task C, with weighted F1-scores of 0.342, 0.784, and 0.535 respectively. The results show the robustness and effectiveness of our framework. Our code is released at github.Comment: De-Factify 2: Second Workshop on Multimodal Fact Checking and Hate Speech Detection, co-located with AAAI 202