Модель гауссовой смеси

method ID: gaussianmixturemodel

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This paper presents an unsupervised word sense learning algorithm, which induces senses of target word by grouping its occurrences into a “natural” number of clusters based on the similarity of …...

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Unsupervised Domain Relevance Estimation for Word Sense Disambiguation

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This paper presents Domain Relevance Estima- tion (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respect to a certain …...

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Unsupervised Information Extraction with Distributional Prior Knowledge

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We address the task of automatic discovery of information extraction template from a given text collection. Our approach clusters candi- date slot fillers to identify meaningful tem- plate slots. We …...

44289

Japanese conversation corpus for training and evaluation of backchannel prediction model.

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AbstractIn this paper, we propose an experimental method for building a specialized corpus for training and evaluating backchannel prediction models of spoken dialogue. To develop a backchannel prediction model using …...

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Wild bird watching has become a popular leisure activity in recent years. Very often, people can see birds or hear their sounds, but have no idea what kind of bird …...

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DT_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output

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AbstractWe describe our system (DT Team) submitted at SemEval-2017 Task 1, Semantic Textual Similarity (STS) challenge for English (Track 5). We developed three different models with various features including similarity …...

25379

Text-dependent Forensic Voice Comparison: Likelihood Ratio Estimation with the Hidden Markov Model (HMM) and Gaussian Mixture Model

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AbstractAmong the more typical forensic voice comparison (FVC) approaches, the acoustic-phonetic statistical approach is suitable for text-dependent FVC, but it does not fully exploit available time-varying information of speech in …...

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Density Matching for Bilingual Word Embedding

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AbstractRecent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach …...

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AbstractMedia plays an important role in shaping public opinion. Biased media can influence people in undesirable directions and hence should be unmasked as such. We observe that feature-based and neural …...

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Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension

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Learning Numeral Embedding

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AbstractUser intent classification plays a vital role in dialogue systems. Since user intent may frequently change over time in many realistic scenarios, unknown (new) intent detection has become an essential …...

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AbstractMemes are widely used on social media. They usually contain multi-modal information such as images and texts, serving as valuable data sources to analyse opinions and sentiment orientations of online …...

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SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training

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AbstractThe conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks. However, in …...

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