A Topological Method for Comparing Document Semantics

• URL: http://arxiv.org/abs/2012.04203v1
• Date: Tue, 8 Dec 2020 04:21:40 GMT
• Title: A Topological Method for Comparing Document Semantics
• Authors: Yuqi Kong, Fanchao Meng, Benjamin Carterette
• Abstract summary: We propose a novel algorithm for comparing semantics similarity between two documents. Our experiments are conducted on a document dataset with human judges' results. Our algorithm can produce highly human-consistent results, and also beats most state-of-the-art methods though ties with NLTK.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Comparing document semantics is one of the toughest tasks in both Natural Language Processing and Information Retrieval. To date, on one hand, the tools for this task are still rare. On the other hand, most relevant methods are devised from the statistic or the vector space model perspectives but nearly none from a topological perspective. In this paper, we hope to make a different sound. A novel algorithm based on topological persistence for comparing semantics similarity between two documents is proposed. Our experiments are conducted on a document dataset with human judges' results. A collection of state-of-the-art methods are selected for comparison. The experimental results show that our algorithm can produce highly human-consistent results, and also beats most state-of-the-art methods though ties with NLTK.

Related papers

• Contextual Document Embeddings [77.22328616983417]
  We propose two complementary methods for contextualized document embeddings. First, an alternative contrastive learning objective that explicitly incorporates the document neighbors into the intra-batch contextual loss. Second, a new contextual architecture that explicitly encodes neighbor document information into the encoded representation.
  arXiv  Detail & Related papers  (2024-10-03T14:33:34Z)
• Relation-aware Ensemble Learning for Knowledge Graph Embedding [68.94900786314666]
  We propose to learn an ensemble by leveraging existing methods in a relation-aware manner. exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. We propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently.
  arXiv  Detail & Related papers  (2023-10-13T07:40:12Z)
• A Comparative Study of Sentence Embedding Models for Assessing Semantic Variation [0.0]
  We compare several recent sentence embedding methods via time-series of semantic similarity between successive sentences and matrices of pairwise sentence similarity for multiple books of literature. We find that most of the sentence embedding methods considered do infer highly correlated patterns of semantic similarity in a given document, but show interesting differences.
  arXiv  Detail & Related papers  (2023-08-08T23:31:10Z)
• Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence [0.0]
  We propose a method that incorporates a deeper understanding of both sentence and document themes. This allows our model to detect latent topics that may include uncommon words or neologisms. We present correlation coefficients with human identification of intruder words and achieve near-human level results at the word-intrusion task.
  arXiv  Detail & Related papers  (2023-03-30T12:24:25Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Fine-Grained Visual Entailment [51.66881737644983]
  We propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image. Unlike prior work, our method is inherently explainable and makes logical predictions at different levels of granularity. We evaluate our method on a new dataset of manually annotated knowledge elements and show that our method achieves 68.18% accuracy at this challenging task.
  arXiv  Detail & Related papers  (2022-03-29T16:09:38Z)
• Learning with Neighbor Consistency for Noisy Labels [69.83857578836769]
  We present a method for learning from noisy labels that leverages similarities between training examples in feature space. We evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-WebVision, Clothing1M, mini-ImageNet-Red) noise.
  arXiv  Detail & Related papers  (2022-02-04T15:46:27Z)
• TFW2V: An Enhanced Document Similarity Method for the Morphologically Rich Finnish Language [0.5801044612920816]
  This study focuses on experimenting with some of the current approaches to Finnish, which is a morphologically rich language. We propose a simple method, TFW2V, which shows high efficiency in handling both long text documents and limited amounts of data.
  arXiv  Detail & Related papers  (2021-12-23T12:27:45Z)
• A novel hybrid methodology of measuring sentence similarity [0.0]
  It is necessary to measure the similarity between sentences accurately. Deep learning methodology shows a state-of-the-art performance in many natural language processing fields. Considering the structure of the sentence or the word structure that makes up the sentence is also important.
  arXiv  Detail & Related papers  (2021-05-03T06:50:54Z)
• Efficient Clustering from Distributions over Topics [0.0]
  We present an approach that relies on the results of a topic modeling algorithm over documents in a collection as a means to identify smaller subsets of documents where the similarity function can be computed. This approach has proved to obtain promising results when identifying similar documents in the domain of scientific publications.
  arXiv  Detail & Related papers  (2020-12-15T10:52:19Z)
• Interpretable Multi-dataset Evaluation for Named Entity Recognition [110.64368106131062]
  We present a general methodology for interpretable evaluation for the named entity recognition (NER) task. The proposed evaluation method enables us to interpret the differences in models and datasets, as well as the interplay between them. By making our analysis tool available, we make it easy for future researchers to run similar analyses and drive progress in this area.
  arXiv  Detail & Related papers  (2020-11-13T10:53:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.