It is well-known that typical word embedding methods such as Word2Vec and GloVe have the property that the meaning can be composed by adding up the embeddings (additive compositionality). Several theories have been proposed to explain additive compositionality, but the following questions remain unanswered: (Q1) The assumptions of those theories do not hold for the practical word embedding. (Q2) Ordinary additive compositionality can be seen as an AND operation of word meanings, but it is not well understood how other operations, such as OR and NOT, can be computed by the embeddings. We address these issues by the idea of frequency-weighted centering at its core. This paper proposes a post-processing method for bridging the gap between practical word embedding and the assumption of theory about additive compositionality as an answer to (Q1). It also gives a method for taking OR or NOT of the meaning by linear operation of word embedding as an answer to (Q2). Moreover, we confirm experimentally that the accuracy of AND operation, i.e., the ordinary additive compositionality, can be improved by our post-processing method (3.5x improvement in top-100 accuracy) and that OR and NOT operations can be performed correctly.

Distributed representations of words encode lexical semantic information, but how is that information encoded in word embeddings? Focusing on the skip-gram with negative-sampling method, we show theoretically and experimentally that the squared norm of word embedding encodes the information gain defined by the Kullback-Leibler divergence of the co-occurrence distribution of a word to the unigram distribution of the corpus. Furthermore, through experiments on tasks of keyword extraction, hypernym prediction, and part-of-speech discrimination, we confirmed that the KL divergence and the squared norm of embedding work as a measure of the informativeness of a word provided that the bias caused by word frequency is adequately corrected.

我们使用释义作为独特的数据来源来分析上下文化的嵌入，特别关注BERT。由于释义自然编码一致的单词和短语语义，因此它们提供了一种独特的镜头来研究嵌入的特性。使用释义数据库的比对，我们在释义和短语表示中研究单词。我们发现，上下文嵌入有效地处理多义单词，但在许多情况下给出了同义词，具有令人惊讶的不同表示。我们证实了先前的发现，即Bert对单词顺序敏感，但是就BERT层的情境化水平而言，发现与先前工作的模式略有不同。

越来越多的自然语言处理研究（NLP）和自然语言理解（NLU）正在研究从大语言模型的嵌入一词中学习或编码的人类知识。这是了解哪些知识语言模型捕获的一步，类似于人类对语言和交流的理解。在这里，我们调查了单词（即价，唤醒，主导地位）的影响以及如何在大型神经网络中预先训练的单词嵌入中编码。我们将人类标记的数据集用作地面真理，并对四种单词嵌入方式进行了各种相关和分类测试。嵌入在静态或上下文化方面有所不同，以及在训练和微调阶段优先考虑特定信息的程度。我们的分析表明，嵌入Vanilla Bert模型的单词并未明显编码英语单词的影响信息。只有在与情绪相关的任务上进行微调或包含来自情感丰富的环境的额外上下文化信息时，只有在bert模型进行微调时，相应的嵌入方式可以编码更相关的影响信息。

Embedding words in vector space is a fundamental first step in state-of-the-art natural language processing (NLP). Typical NLP solutions employ pre-defined vector representations to improve generalization by co-locating similar words in vector space. For instance, Word2Vec is a self-supervised predictive model that captures the context of words using a neural network. Similarly, GLoVe is a popular unsupervised model incorporating corpus-wide word co-occurrence statistics. Such word embedding has significantly boosted important NLP tasks, including sentiment analysis, document classification, and machine translation. However, the embeddings are dense floating-point vectors, making them expensive to compute and difficult to interpret. In this paper, we instead propose to represent the semantics of words with a few defining words that are related using propositional logic. To produce such logical embeddings, we introduce a Tsetlin Machine-based autoencoder that learns logical clauses self-supervised. The clauses consist of contextual words like "black," "cup," and "hot" to define other words like "coffee," thus being human-understandable. We evaluate our embedding approach on several intrinsic and extrinsic benchmarks, outperforming GLoVe on six classification tasks. Furthermore, we investigate the interpretability of our embedding using the logical representations acquired during training. We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.

基于变压器的语言模型最近在许多自然语言任务中取得了显着的结果。但是，通常通过利用大量培训数据来实现排行榜的性能，并且很少通过将明确的语言知识编码为神经模型。这使许多人质疑语言学对现代自然语言处理的相关性。在本文中，我介绍了几个案例研究，以说明理论语言学和神经语言模型仍然相互关联。首先，语言模型通过提供一个客观的工具来测量语义距离，这对语言学家很有用，语义距离很难使用传统方法。另一方面，语言理论通过提供框架和数据源来探究我们的语言模型，以了解语言理解的特定方面，从而有助于语言建模研究。本论文贡献了三项研究，探讨了语言模型中语法 - 听觉界面的不同方面。在论文的第一部分中，我将语言模型应用于单词类灵活性的问题。我将Mbert作为语义距离测量的来源，我提供了有利于将单词类灵活性分析为方向过程的证据。在论文的第二部分中，我提出了一种方法来测量语言模型中间层的惊奇方法。我的实验表明，包含形态句法异常的句子触发了语言模型早期的惊喜，而不是语义和常识异常。最后，在论文的第三部分中，我适应了一些心理语言学研究，以表明语言模型包含了论证结构结构的知识。总而言之，我的论文在自然语言处理，语言理论和心理语言学之间建立了新的联系，以为语言模型的解释提供新的观点。

Word Mover的距离（WMD）计算单词和模型之间的距离与两个文本序列中的单词之间的移动成本相似。但是，它在句子相似性评估中没有提供良好的性能，因为它不包含单词重要性，并且在句子中未能将固有的上下文和结构信息纳入句子。提出了一种使用语法解析树（称为语法感知单词Mover的距离（SYNWMD））的改进的WMD方法，以解决这项工作中的这两个缺点。首先，基于从句子树的句法解析树中提取的一词共发生统计量建立了加权图。每个单词的重要性是从图形连接性推断出的。其次，在计算单词之间的距离时，考虑了单词的局部句法解析结构。为了证明拟议的SynWMD的有效性，我们对6个文本语义相似性（STS）数据集和4个句子分类数据集进行了实验。实验结果表明，SynWMD在STS任务上实现了最先进的性能。它还在句子分类任务上胜过其他基于WMD的方法。

Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. Nevertheless, traditional models often fall short in intrinsic issues of linguistics, such as polysemy and homonymy. Any expert system that makes use of natural language in its core, can be affected by a weak semantic representation of text, resulting in inaccurate outcomes based on poor decisions. To mitigate such issues, we propose a novel approach called Most Suitable Sense Annotation (MSSA), that disambiguates and annotates each word by its specific sense, considering the semantic effects of its context. Our approach brings three main contributions to the semantic representation scenario: (i) an unsupervised technique that disambiguates and annotates words by their senses, (ii) a multi-sense embeddings model that can be extended to any traditional word embeddings algorithm, and (iii) a recurrent methodology that allows our models to be re-used and their representations refined. We test our approach on six different benchmarks for the word similarity task, showing that our approach can produce state-of-the-art results and outperforms several more complex state-of-the-art systems.

最近进行了许多研究来调查语境语言模型的属性，但令人惊讶的是，其中只有少数人在语义相似性方面考虑这些模型的属性。在本文中，我们首先通过利用Semcor和Wordnet范式关系的受控背景下的探测机制来专注于英语的这些属性。然后，我们建议将相同的方法调整到更开放的设置，以表征静态和上下文模型之间的差异。

Measuring the semantic similarity between two sentences is still an important task. The word mover's distance (WMD) computes the similarity via the optimal alignment between the sets of word embeddings. However, WMD does not utilize word order, making it difficult to distinguish sentences with large overlaps of similar words, even if they are semantically very different. Here, we attempt to improve WMD by incorporating the sentence structure represented by BERT's self-attention matrix (SAM). The proposed method is based on the Fused Gromov-Wasserstein distance, which simultaneously considers the similarity of the word embedding and the SAM for calculating the optimal transport between two sentences. Experiments on paraphrase identification and semantic textual similarity show that the proposed method improves WMD and its variants. Our code is available at https://github.com/ymgw55/WSMD.

Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.

大多数无监督的NLP模型代表了语义空间中单点或单个区域的每个单词，而现有的多感觉单词嵌入物不能代表像素序或句子等更长的单词序列。我们提出了一种用于文本序列（短语或句子）的新型嵌入方法，其中每个序列由一个不同的多模码本嵌入物组表示，以捕获其含义的不同语义面。码本嵌入式可以被视为集群中心，该中心总结了在预训练的单词嵌入空间中的可能共同出现的单词的分布。我们介绍了一个端到端的训练神经模型，直接从测试时间内从输入文本序列预测集群中心集。我们的实验表明，每句话码本嵌入式显着提高无监督句子相似性和提取摘要基准的性能。在短语相似之处实验中，我们发现多面嵌入物提供可解释的语义表示，但不优于单面基线。

Future work sentences (FWS) are the particular sentences in academic papers that contain the author's description of their proposed follow-up research direction. This paper presents methods to automatically extract FWS from academic papers and classify them according to the different future directions embodied in the paper's content. FWS recognition methods will enable subsequent researchers to locate future work sentences more accurately and quickly and reduce the time and cost of acquiring the corpus. The current work on automatic identification of future work sentences is relatively small, and the existing research cannot accurately identify FWS from academic papers, and thus cannot conduct data mining on a large scale. Furthermore, there are many aspects to the content of future work, and the subdivision of the content is conducive to the analysis of specific development directions. In this paper, Nature Language Processing (NLP) is used as a case study, and FWS are extracted from academic papers and classified into different types. We manually build an annotated corpus with six different types of FWS. Then, automatic recognition and classification of FWS are implemented using machine learning models, and the performance of these models is compared based on the evaluation metrics. The results show that the Bernoulli Bayesian model has the best performance in the automatic recognition task, with the Macro F1 reaching 90.73%, and the SCIBERT model has the best performance in the automatic classification task, with the weighted average F1 reaching 72.63%. Finally, we extract keywords from FWS and gain a deep understanding of the key content described in FWS, and we also demonstrate that content determination in FWS will be reflected in the subsequent research work by measuring the similarity between future work sentences and the abstracts.

在NLP社区中有一个正在进行的辩论，无论现代语言模型是否包含语言知识，通过所谓的探针恢复。在本文中，我们研究了语言知识是否是现代语言模型良好表现的必要条件，我们称之为\ Texit {重新发现假设}。首先，我们展示了语言模型，这是显着压缩的，但在预先磨普目标上表现良好，以便在语言结构探讨时保持良好的分数。这一结果支持重新发现的假设，并导致我们的论文的第二款贡献：一个信息 - 理论框架，与语言建模目标相关。该框架还提供了测量语言信息对字词预测任务的影响的度量标准。我们通过英语综合和真正的NLP任务加固我们的分析结果。

The relationship between words in a sentence often tells us more about the underlying semantic content of a document than its actual words, individually. In this work, we propose two novel algorithms, called Flexible Lexical Chain II and Fixed Lexical Chain II. These algorithms combine the semantic relations derived from lexical chains, prior knowledge from lexical databases, and the robustness of the distributional hypothesis in word embeddings as building blocks forming a single system. In short, our approach has three main contributions: (i) a set of techniques that fully integrate word embeddings and lexical chains; (ii) a more robust semantic representation that considers the latent relation between words in a document; and (iii) lightweight word embeddings models that can be extended to any natural language task. We intend to assess the knowledge of pre-trained models to evaluate their robustness in the document classification task. The proposed techniques are tested against seven word embeddings algorithms using five different machine learning classifiers over six scenarios in the document classification task. Our results show the integration between lexical chains and word embeddings representations sustain state-of-the-art results, even against more complex systems.

在过去几年中，Word和句嵌入式已建立为各种NLP任务的文本预处理，并显着提高了性能。不幸的是，还表明这些嵌入物从训练数据中继承了各种偏见，从而通过了社会中存在的偏差到NLP解决方案。许多论文试图在单词或句子嵌入中量化偏差，以评估脱叠方法或比较不同的嵌入模型，通常具有基于余弦的指标。然而，最近有些作品对这些指标提出了疑虑，表明即使这些指标报告低偏见，其他测试仍然显示偏差。事实上，文献中提出了各种各样的偏差指标或测试，而没有任何关于最佳解决方案的共识。然而，我们缺乏评估理论级别的偏见度量或详细阐述不同偏差度量的优缺点的作品。在这项工作中，我们将探索基于余弦的偏差指标。我们根据以前的作品和偏见度量的推导条件的思想形式化偏差定义。此外，我们彻底调查了现有的基于余弦的指标及其限制，以表明为什么这些度量可以在某些情况下报告偏差。最后，我们提出了一个新的公制，同样地解决现有度量的缺点，以及数学上证明的表现相同。

观察到对于某些NLP任务，例如语义角色预测或主题拟合估计，随机嵌入性能以及预处理的嵌入方式，我们探索了哪些设置允许并检查大多数学习的编码：语义角色，语义角色，语义角色嵌入或``网络''。我们发现细微的答案，具体取决于任务及其与培训目标的关系。我们研究了多任务学习中的这些表示学习方面，在这些方面，角色预测和角色填充是受监督的任务，而几个主题拟合任务不在模型的直接监督之外。我们观察到某些任务的质量得分与培训数据规模之间的非单调关系。为了更好地理解此观察结果，我们使用这些任务的每个动力版本来分析这些结果。

We analyze skip-gram with negative-sampling (SGNS), a word embedding method introduced by Mikolov et al., and show that it is implicitly factorizing a word-context matrix, whose cells are the pointwise mutual information (PMI) of the respective word and context pairs, shifted by a global constant. We find that another embedding method, NCE, is implicitly factorizing a similar matrix, where each cell is the (shifted) log conditional probability of a word given its context. We show that using a sparse Shifted Positive PMI word-context matrix to represent words improves results on two word similarity tasks and one of two analogy tasks. When dense low-dimensional vectors are preferred, exact factorization with SVD can achieve solutions that are at least as good as SGNS's solutions for word similarity tasks. On analogy questions SGNS remains superior to SVD. We conjecture that this stems from the weighted nature of SGNS's factorization.

The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.