Word meaning may change over time as a reflection of changes in human society. Therefore, modeling time in word representation is necessary for some diachronic tasks. Most existing diachronic word representation approaches train the embeddings separately for each pre-grouped time-stamped corpus and align these embeddings, e.g., by orthogonal projections, vector initialization, temporal referencing, and compass. However, not only does word meaning change in a short time, word meaning may also be subject to evolution over long timespans, thus resulting in a unified continuous process. A recent approach called'DiffTime' models semantic evolution as functions parameterized by multiple-layer nonlinear neural networks over time. In this paper, we will carry on this line of work by learning explicit functions over time for each word. Our approach, called'Word2Fun', reduces the space complexity from O(TVD) to O(kVD) where kis a small constant (k T). In particular, a specific instance based on polynomial functions could provably approximate any function modeling word evolution with a given negligible error thanks to the Weierstrass Approximation Theorem. The effectiveness of the proposed approach is evaluated in diverse tasks including timeaware word clustering, temporal analogy, and semantic change detection.

Computer vision has benefited from initializing multiple deep layers with weights pretrained on large supervised training sets like ImageNet. Natural language processing (NLP) typically sees initialization of only the lowest layer of deep models with pretrained word vectors. In this paper, we use a deep LSTM encoder from an attentional sequence-to-sequence model trained for machine translation (MT) to contextualize word vectors. We show that adding these context vectors (CoVe) improves performance over using only unsupervised word and character vectors on a wide variety of common NLP tasks: sentiment analysis (SST, IMDb), question classification (TREC), entailment (SNLI), and question answering (SQuAD). For fine-grained sentiment analysis and entailment, CoVe improves performance of our baseline models to the state of the art.

The word embedding space in neural models is skewed, and correcting this can improve task performance.

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This paper delves into the intricate world of Urdu poetry, exploring its thematic depths through a lens of polysemy. By focusing on the nuanced differences between three seemingly synonymous words (pyaar, muhabbat, and ishq) we expose a spectrum of emotions and experiences unique to the Urdu language. This study employs a polysemic case study approach, meticulously examining how these words are interwoven within the rich tapestry of Urdu poetry. By analyzing their usage and context, we uncover a hidden layer of meaning, revealing subtle distinctions which lack direct equivalents in English literature. Furthermore, we embark on a comparative analysis, generating word embeddings for both Urdu and English terms related to love. This enables us to quantify and visualize the semantic space occupied by these words, providing valuable insights into the cultural and linguistic nuances of expressing love. Through this multifaceted approach, our study sheds light on the captivating complexities of Urdu poetry, offering a deeper understanding and appreciation for its unique portrayal of love and its myriad expressions

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. Paper For paper 1180: Deep Recursive Neural Networks for Compositionality in Language This paper introduces a new architecture -- deep recursive neural network (deep RNN) which is constructed by stacking multiple recursive layers. The authors evaluate the proposed model on the task of fine-grained sentiment classification. Clarity - In general, this paper is well written and pleasant to read. Quality - The paper seems technically sound.

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