Иерархиялық кластерлеу

method ID: hierarchicalclustering

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Word-Sense Classification by Hierarchical Clustering

Authors: nan

Publication: TIPSTER (1998)

DOI: nan

This paper investigates the use of clustering techniques in word-sense classification, which identifies different contexts that a word was used with the same or similar sense. For simplicity, we have …...

18230

Language Model-Based Document Clustering Using Random Walks

Authors: nan

Publication: TIPSTER (2006)

DOI: nan

We propose a new document vector represen- tation specifically designed for the document clustering task. Instead of the traditional term- based vectors, a document is represented as an �-dimensional vector, …...

24176

A clustering approach for translationese identification

Authors: nan

Publication: TIPSTER (2013)

DOI: nan

Our paper is concerned with investigating the impact of translationese on the novels of a bilingual writer and asking whether one could determine the authorship of a translated document. The …...

28871

Distributed Representations for Unsupervised Semantic Role Labeling

Authors: nan

Publication: TIPSTER (2015)

DOI: nan

We present a new approach for unsuper- vised semantic role labeling that lever- ages distributed representations. We in- duce embeddings to represent a predi- cate, its arguments and their complex …...

34232

Advancing Linguistic Features and Insights by Label-informed Feature Grouping: An Exploration in the Context of Native Language Identification

Authors: nan

Publication: TIPSTER (2016)

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AbstractWe propose a hierarchical clustering approach designed to group linguistic features for supervised machine learning that is inspired by variationist linguistics. The method makes it possible to abstract away from …...

4545

Clustering Paraphrases by Word Sense

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

Automatically generated databases of English paraphrases have the drawback that they re- turn a single list of paraphrases for an input word or phrase. This means that all senses of …...

22933

SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives

Authors: nan

Publication: TIPSTER (2016)

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AbstractAn important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions. …...

4364

A phoneme clustering algorithm based on the obligatory contour principle

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractThis paper explores a divisive hierarchical clustering algorithm based on the well-known Obligatory Contour Principle in phonology. The purpose is twofold: to see if such an algorithm could be used …...

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Encoding of phonology in a recurrent neural model of grounded speech

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Publication: TIPSTER (2017)

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AbstractWe study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects …...

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Triad-based Neural Network for Coreference Resolution

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Publication: TIPSTER (2018)

DOI: nan

AbstractWe propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical …...

5511

Few-shot Pseudo-Labeling for Intent Detection

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AbstractIn this paper, we introduce a state-of-the-art pseudo-labeling technique for few-shot intent detection. We devise a folding/unfolding hierarchical clustering algorithm which assigns weighted pseudo-labels to unlabeled user utterances. We show …...

3927

Unsupervised Multi-document Summarization for News Corpus with Key Synonyms and Contextual Embeddings

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1055

COVID-19 Mythbusters in World Languages

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AbstractThis paper introduces a multi-lingual database containing translated texts of COVID-19 mythbusters. The database has translations into 115 languages as well as the original English texts, of which the original …...

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Arguably at SemEval-2023 Task 11: Learning the disagreements using unsupervised behavioral clustering and language models

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AbstractWe describe SemEval-2023 Task 11 on behavioral segregation of annotations to find the similarities and contextual thinking of a group of annotators. We have utilized a behavioral segmentation analysis on …...

24676