We apply entropy agglomeration (EA), a recently introduced algorithm, to cluster the words of a literary text. EA is a greedy agglomerative procedure that minimizes projection entropy (PE), a function that can quantify the segmentedness of an element set. To apply it, the text is reduced to a feature allocation, a combinatorial object to represent the word occurences in the text's paragraphs. The experiment results demonstrate that EA, despite its reduction and simplicity, is useful in capturing significant relationships among the words in the text. This procedure was implemented in Python and published as a free software: REBUS.
We present a clustering-based language model using word embeddings for text readability prediction. Presumably, an Euclidean semantic space hypothesis holds true for word embeddings whose training is done by observing word co-occurrences. We argue that clustering with word embeddings in the metric space should yield feature representations in a higher semantic space appropriate for text regression. Also, by representing features in terms of histograms, our approach can naturally address documents of varying lengths. An empirical evaluation using the Common Core Standards corpus reveals that th…
In this digital era, almost in every discipline people are using automated systems that generate information represented in document format in different natural languages. As a result, there is a growing interest towards better solutions for finding, organizing and analyzing these documents. In this paper, we propose a system that clusters Amharic text documents using Encyclopedic Knowledge (EK) with neural word embedding. EK enables the representation of related concepts and neural word embedding allows us to handle the contexts of the relatedness. During the clustering process, all the text …
In data dominated systems and applications, a concept of representing words in a numerical format has gained a lot of attention. There are a few approaches used to generate such a representation. An interesting issue that should be considered is the ability of such representations - called embeddings - to imitate human-based semantic similarity between words. In this study, we perform a fuzzy-based analysis of vector representations of words, i.e., word embeddings. We use two popular fuzzy clustering algorithms on count-based word embeddings, known as GloVe, of different dimensionality. Words …
Shahjalal University of Science and Technology（沙赫贾拉勒科技大学）
In this paper, we describe a research method that generates Bangla word clusters on the basis of relating to meaning in language and contextual similarity. The importance of word clustering is in parts of speech (POS) tagging, word sense disambiguation, text classification, recommender system, spell checker, grammar checker, knowledge discover and for many others Natural Language Processing (NLP) applications. In the history of word clustering, English and some other languages have already implemented some methods on word clustering efficiently. But due to lack of the resources, word clusterin…
A visual observation on the geometry of UMAP projections of the difference vectors of antonym and synonym word pair embeddings
Faculty of Information Technology, University of Jyväskylä（于韦斯屈莱大学信息科技学院）；HUS Helsinki University Hospital（赫尔辛基大学医院）；Digital Workforce Services（数字劳动力服务）
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 sp…
Multilingual Neural Machine Translation has been showing great success using transformer models. Deploying these models is challenging because they usually require large vocabulary (vocab) sizes for various languages. This limits the speed of predicting the output tokens in the last vocab projection layer. To alleviate these challenges, this paper proposes a fast vocabulary projection method via clustering which can be used for multilingual transformers on GPUs. First, we offline split the vocab search space into disjoint clusters given the hidden context vector of the decoder output, which re…
Mila, Université de Montréal（米拉-蒙特利尔大学）；Amazon Alexa（亚马逊Alexa）；Google（谷歌）
Recently, there has been a strong interest in developing natural language applications that live on personal devices such as mobile phones, watches and IoT with the objective to preserve user privacy and have low memory. Advances in Locality-Sensitive Hashing (LSH)-based projection networks have demonstrated state-of-the-art performance in various classification tasks without explicit word (or word-piece) embedding lookup tables by computing on-the-fly text representations. In this paper, we show that the projection based neural classifiers are inherently robust to misspellings and perturbatio…
The paper reports our participation in the shared task on word sense induction and disambiguation for the Russian language (RUSSE-2018). Our team was ranked 2nd for the wiki-wiki dataset (containing mostly homonyms) and 5th for the bts-rnc and active-dict datasets (containing mostly polysemous words) among all 19 participants. The method we employed was extremely naive. It implied representing contexts of ambiguous words as averaged word embedding vectors, using off-the-shelf pre-trained distributional models. Then, these vector representations were clustered with mainstream clustering techniq…
This paper presents a new Bayesian non-parametric model by extending the usage of Hierarchical Dirichlet Allocation to extract tree structured word clusters from text data. The inference algorithm of the model collects words in a cluster if they share similar distribution over documents. In our experiments, we observed meaningful hierarchical structures on NIPS corpus and radiology reports collected from public repositories.
The paper presents a method for word sense disambiguation based on parallel corpora. The method exploits recent advances in word alignment and word clustering based on automatic extraction of translation equivalents and being supported by available aligned wordnets for the languages in the corpus. The wordnets are aligned to the Princeton Wordnet, according to the principles established by EuroWordNet. The evaluation of the WSD system, implementing the method described herein showed very encouraging results. The same system used in a validation mode, can be used to check and spot alignment err…
In this article I proposed a new model to achieve Chinese word segmentation(CWS),which may have the potentiality to apply in other domains in the future.It is a new thinking in CWS compared to previous works,to consider it as a clustering problem instead of a labeling problem.In this model,LSTM and self attention structures are used to collect context also sentence level features in every layer,and after several layers,a clustering model is applied to split characters into groups,which are the final segmentation results.I call this model CLNN.This algorithm can reach 98 percent of F score (wit…
Charles University（查理大学）；Faculty of Mathematics and Physics（数学与物理学院）；Institute of Formal and Applied Linguistics（形式与应用语言学研究所）
We focus on the task of unsupervised lemmatization, i.e. grouping together inflected forms of one word under one label (a lemma) without the use of annotated training data. We propose to perform agglomerative clustering of word forms with a novel distance measure. Our distance measure is based on the observation that inflections of the same word tend to be similar both string-wise and in meaning. We therefore combine word embedding cosine similarity, serving as a proxy to the meaning similarity, with Jaro-Winkler edit distance. Our experiments on 23 languages show our approach to be promising,…
Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings. However, there are two potential drawbacks to these methods: one is that they focus too much on the superficial information of the frame-evoking verb and the other is that they tend to divide the instances of the same verb into too many different frame clusters. To overcome these drawbacks, we propose a semantic frame induction method using masked word embeddings and two-step clustering. Through experiments on the English Fram…
We describe and experimentally evaluate a method for automatically clustering words according to their distribution in particular syntactic contexts. Deterministic annealing is used to find lowest distortion sets of clusters. As the annealing parameter increases, existing clusters become unstable and subdivide, yielding a hierarchical ``soft'' clustering of the data. Clusters are used as the basis for class models of word coocurrence, and the models evaluated with respect to held-out test data.
University of Bari（巴里大学）；Dublin City University（都柏林城市大学）
This paper describes the system proposed for the SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection. We focused our approach on the detection problem. Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or loosed senses. To this end, we defined a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compared the proposed approach with a number of similarity-based thresholds. We found that…
In the new era of internet systems and applications, a concept of detecting distinguished topics from huge amounts of text has gained a lot of attention. These methods use representation of text in a numerical format -- called embeddings -- to imitate human-based semantic similarity between words. In this study, we perform a fuzzy-based analysis of various vector representations of words, i.e., word embeddings. Also we introduce new methods of fuzzy clustering based on hybrid implementation of fuzzy clustering methods with an evolutionary algorithm named Forest Optimization. We use two popular…
University of Bucharest（布加勒斯特大学）；Romanian Young Academy（罗马尼亚青年科学院）
Romanian is one of the understudied languages in computational linguistics, with few resources available for the development of natural language processing tools. In this paper, we introduce LaRoSeDa, a Large Romanian Sentiment Data Set, which is composed of 15,000 positive and negative reviews collected from one of the largest Romanian e-commerce platforms. We employ two sentiment classification methods as baselines for our new data set, one based on low-level features (character n-grams) and one based on high-level features (bag-of-word-embeddings generated by clustering word embeddings with…
In this paper, we propose a novel approach for text classification based on clustering word embeddings, inspired by the bag of visual words model, which is widely used in computer vision. After each word in a collection of documents is represented as word vector using a pre-trained word embeddings model, a k-means algorithm is applied on the word vectors in order to obtain a fixed-size set of clusters. The centroid of each cluster is interpreted as a super word embedding that embodies all the semantically related word vectors in a certain region of the embedding space. Every embedded word in t…
Mila, Université de Montréal（蒙特利尔大学米拉研究所）；Google Research（谷歌研究院）；Google（谷歌）
Neural word representations are at the core of many state-of-the-art natural language processing models. A widely used approach is to pre-train, store and look up word or character embedding matrices. While useful, such representations occupy huge memory making it hard to deploy on-device and often do not generalize to unknown words due to vocabulary pruning. In this paper, we propose a skip-gram based architecture coupled with Locality-Sensitive Hashing (LSH) projections to learn efficient dynamically computable representations. Our model does not need to store lookup tables as representation…
We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument cluster…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Cornell University（康奈尔大学）
Clustering token-level contextualized word representations produces output that shares many similarities with topic models for English text collections. Unlike clusterings of vocabulary-level word embeddings, the resulting models more naturally capture polysemy and can be used as a way of organizing documents. We evaluate token clusterings trained from several different output layers of popular contextualized language models. We find that BERT and GPT-2 produce high quality clusterings, but RoBERTa does not. These cluster models are simple, reliable, and can perform as well as, if not better t…
University of Science and Technology of China（中国科学技术大学）；Microsoft Research（微软研究院）
Word embedding, which refers to low-dimensional dense vector representations of natural words, has demonstrated its power in many natural language processing tasks. However, it may suffer from the inaccurate and incomplete information contained in the free text corpus as training data. To tackle this challenge, there have been quite a few works that leverage knowledge graphs as an additional information source to improve the quality of word embedding. Although these works have achieved certain success, they have neglected some important facts about knowledge graphs: (i) many relationships in k…
Natural language processing area is still under research. But now a day it is on platform for worldwide researchers. Natural language processing includes analyzing the language based on its structure and then tagging of each word appropriately with its grammar base. Here we have 50,000 tagged words set and we try to cluster those Gujarati words based on proposed algorithm, we have defined our own algorithm for processing. Many clustering techniques are available Ex. Single linkage, complete, linkage,average linkage, Hear no of clusters to be formed are not known, so it is all depends on the ty…
Contextualized word representations have proven useful for various natural language processing tasks. However, it remains unclear to what extent these representations can cover hand-coded semantic information such as semantic frames, which specify the semantic role of the arguments associated with a predicate. In this paper, we focus on verbs that evoke different frames depending on the context, and we investigate how well contextualized word representations can recognize the difference of frames that the same verb evokes. We also explore which types of representation are suitable for semantic…
University of Oslo（奥斯陆大学）；University of Helsinki（赫尔辛基大学）；National Research University Higher School of Economics（国立研究型大学高等经济学院）
We present our experience in applying distributional semantics (neural word embeddings) to the problem of representing and clustering documents in a bilingual comparable corpus. Our data is a collection of Russian and Ukrainian academic texts, for which topics are their academic fields. In order to build language-independent semantic representations of these documents, we train neural distributional models on monolingual corpora and learn the optimal linear transformation of vectors from one language to another. The resulting vectors are then used to produce `semantic fingerprints' of document…
Brown clustering is a hard, hierarchical, bottom-up clustering of words in a vocabulary. Words are assigned to clusters based on their usage pattern in a given corpus. The resulting clusters and hierarchical structure can be used in constructing class-based language models and for generating features to be used in NLP tasks. Because of its high computational cost, the most-used version of Brown clustering is a greedy algorithm that uses a window to restrict its search space. Like other clustering algorithms, Brown clustering finds a sub-optimal, but nonetheless effective, mapping of words to c…
We describe our language-independent unsupervised word sense induction system. This system only uses topic features to cluster different word senses in their global context topic space. Using unlabeled data, this system trains a latent Dirichlet allocation (LDA) topic model then uses it to infer the topics distribution of the test instances. By clustering these topics distributions in their topic space we cluster them into different senses. Our hypothesis is that closeness in topic space reflects similarity between different word senses. This system participated in SemEval-2 word sense inducti…
We address the problem of automatically constructing a thesaurus (hierarchically clustering words) based on corpus data. We view the problem of clustering words as that of estimating a joint distribution over the Cartesian product of a partition of a set of nouns and a partition of a set of verbs, and propose an estimation algorithm using simulated annealing with an energy function based on the Minimum Description Length (MDL) Principle. We empirically compared the performance of our method based on the MDL Principle against that of one based on the Maximum Likelihood Estimator, and found that…
While the use of cluster features became ubiquitous in core NLP tasks, most cluster features in NLP are based on distributional similarity. We propose a new type of clustering criteria, specific to the task of part-of-speech tagging. Instead of distributional similarity, these clusters are based on the beha vior of a baseline tagger when applied to a large corpus. These cluster features provide similar gains in accuracy to those achieved by distributional-similarity derived clusters. Using both types of cluster features together further improve tagging accuracies. We show that the method is ef…
CTG-KrEW: Generating Synthetic Structured Contextually Correlated Content by Conditional Tabular GAN with K-Means Clustering and Efficient Word Embedding
Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；Missouri University of Science and Technology（密苏里科技大学）
We address the problem of clustering words (or constructing a thesaurus) based on co-occurrence data, and using the acquired word classes to improve the accuracy of syntactic disambiguation. We view this problem as that of estimating a joint probability distribution specifying the joint probabilities of word pairs, such as noun verb pairs. We propose an efficient algorithm based on the Minimum Description Length (MDL) principle for estimating such a probability distribution. Our method is a natural extension of those proposed in (Brown et al 92) and (Li & Abe 96), and overcomes their drawbacks…
Mila, Université de Montréal（蒙特利尔大学米拉研究所）；Amazon Alexa（亚马逊Alexa）；Google（谷歌）
At the heart of text based neural models lay word representations, which are powerful but occupy a lot of memory making it challenging to deploy to devices with memory constraints such as mobile phones, watches and IoT. To surmount these challenges, we introduce ProFormer -- a projection based transformer architecture that is faster and lighter making it suitable to deploy to memory constraint devices and preserve user privacy. We use LSH projection layer to dynamically generate word representations on-the-fly without embedding lookup tables leading to significant memory footprint reduction fr…
Harvard University（哈佛大学）；McGovern Institute for Brain Research, Massachusetts Institute of Technology（麻省理工学院麦戈文脑研究所）；Siemens Healthineers（西门子医疗）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
The words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals. However, language can represent only a subset of our rich and detailed cognitive architecture. Here, we ask what kinds of common knowledge (semantic memory) are captured by word meanings (lexical semantics). We examine a prominent computational model that represents words as vectors in a multidimensional space, such that proximity between word-vectors approximates semantic relatedness. Because related words appear in similar contexts, such spaces - called "word embeddings" - c…
Johns Hopkins University（约翰斯·霍普金斯大学）；Department of Computer Science（计算机科学系）；Baltimore, MD, USA（美国马里兰州巴尔的摩）
Topic models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a generative story, but in this paper we propose an alternative way to obtain topics: clustering pre-trained word embeddings while incorporating document information for weighted clustering and reranking top words. We provide benchmarks for the combination of different word embeddings and clustering algorithms, and analyse their performance under dimensionality reduction with PCA. The best performing combination for our appr…
Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time. Unsupervised semantic shift tracing, focal point of SemEval2020, is particularly challenging. Given the unsupervised setup, in this work, we propose to identify clusters among different occurrences of each target word, considering these as representatives of different word meanings. As such, disagreements in obtained clusters naturally allow to quantify the level of semantic shift per each target word in four target languages. To leverage this idea,…
Now a days, the text document is spontaneously increasing over the internet, e-mail and web pages and they are stored in the electronic database format. To arrange and browse the document it becomes difficult. To overcome such problem the document preprocessing, term selection, attribute reduction and maintaining the relationship between the important terms using background knowledge, WordNet, becomes an important parameters in data mining. In these paper the different stages are formed, firstly the document preprocessing is done by removing stop words, stemming is performed using porter stemm…
Stellenbosch University（斯泰伦博斯大学）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）；University of Edinburgh（爱丁堡大学）
Unsupervised segmentation and clustering of unlabelled speech are core problems in zero-resource speech processing. Most approaches lie at methodological extremes: some use probabilistic Bayesian models with convergence guarantees, while others opt for more efficient heuristic techniques. Despite competitive performance in previous work, the full Bayesian approach is difficult to scale to large speech corpora. We introduce an approximation to a recent Bayesian model that still has a clear objective function but improves efficiency by using hard clustering and segmentation rather than full Baye…
University of Technology Sydney（悉尼科技大学）；Eindhoven University of Technology（埃因霍温理工大学）；University of Wollongong（伍伦贡大学）
Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality contextualized document representations, do we really need sophisticated neural models to obtain coherent and interpretable topics? In this paper, we conduct thorough experiments showing that directly clustering high-quality sentence embeddings with an appropriate word selecting method can generate more coherent and diverse topics than NTMs, achieving also higher efficiency and simplicity.
We present Regularized Linear Embedding (RLE), a novel method that projects a collection of linked documents (e.g. citation network) into a pretrained word embedding space. In addition to the textual content, we leverage a matrix of pairwise similarities providing complementary information (e.g., the network proximity of two documents in a citation graph). We first build a simple word vector average for each document, and we use the similarities to alter this average representation. The document representations can help to solve many information retrieval tasks, such as recommendation, classif…
We present RuDSI, a new benchmark for word sense induction (WSI) in Russian. The dataset was created using manual annotation and semi-automatic clustering of Word Usage Graphs (WUGs). Unlike prior WSI datasets for Russian, RuDSI is completely data-driven (based on texts from Russian National Corpus), with no external word senses imposed on annotators. Depending on the parameters of graph clustering, different derivative datasets can be produced from raw annotation. We report the performance that several baseline WSI methods obtain on RuDSI and discuss possibilities for improving these scores.
We propose a new fast word embedding technique using hash functions. The method is a derandomization of a new type of random projections: By disregarding the classic constraint used in designing random projections (i.e., preserving pairwise distances in a particular normed space), our solution exploits extremely sparse non-negative random projections. Our experiments show that the proposed method can achieve competitive results, comparable to neural embedding learning techniques, however, with only a fraction of the computational complexity of these methods. While the proposed derandomization …
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Pittsburgh（匹兹堡大学）
Distributional text clustering delivers semantically informative representations and captures the relevance between each word and semantic clustering centroids. We extend the neural text clustering approach to text classification tasks by inducing cluster centers via a latent variable model and interacting with distributional word embeddings, to enrich the representation of tokens and measure the relatedness between tokens and each learnable cluster centroid. The proposed method jointly learns word clustering centroids and clustering-token alignments, achieving the state of the art results on …
A stochastic model of short-term verbal memory is proposed, in which the psychological state of the subject is encoded as the instantaneous position of a particle diffusing over a semantic graph with a probabilistic structure. The model is particularly suitable for studying the dependence of free-recall observables on semantic properties of the words to be recalled. Besides predicting some well-known experimental features (contiguity effect, forward asymmetry, word-length effect), a novel prediction is obtained on the relationship between the contiguity effect and the syllabic length of words;…
The task of organizing and clustering multilingual news articles for media monitoring is essential to follow news stories in real time. Most approaches to this task focus on high-resource languages (mostly English), with low-resource languages being disregarded. With that in mind, we present a much simpler online system that is able to cluster an incoming stream of documents without depending on language-specific features. We empirically demonstrate that the use of multilingual contextual embeddings as the document representation significantly improves clustering quality. We challenge previous…
Graph-based Clustering for Detecting Semantic Change Across Time and Languages
Faculty of Mathematics and Computer Science, Heidelberg University（海德堡大学数学与计算机科学学院）；Heidelberg Institute for Theoretical Studies（海德堡理论研究所）；Department of Computing Science, University of Aberdeen（阿伯丁大学计算科学学院）
Considering that words with different characteristic in the text have different importance for classification, grouping them together separately can strengthen the semantic expression of each part. Thus we propose a new text representation scheme by clustering words according to their latent semantics and composing them together to get a set of cluster vectors, which are then concatenated as the final text representation. Evaluation on five classification benchmarks proves the effectiveness of our method. We further conduct visualization analysis showing statistical clustering results and veri…
University of Illinois-Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）
Many tasks in Natural Language Processing involve recognizing lexical entailment. Two different approaches to this problem have been proposed recently that are quite different from each other. The first is an asymmetric similarity measure designed to give high scores when the contexts of the narrower term in the entailment are a subset of those of the broader term. The second is a supervised approach where a classifier is learned to predict entailment given a concatenated latent vector representation of the word. Both of these approaches are vector space models that use a single context vector…
Bar Ilan University（巴伊兰大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究所）
Word sense induction (WSI) is the task of unsupervised clustering of word usages within a sentence to distinguish senses. Recent work obtain strong results by clustering lexical substitutes derived from pre-trained RNN language models (ELMo). Adapting the method to BERT improves the scores even further. We extend the previous method to support a dynamic rather than a fixed number of clusters as supported by other prominent methods, and propose a method for interpreting the resulting clusters by associating them with their most informative substitutes. We then perform extensive error analysis r…
