106 research outputs found
A hybrid representation based simile component extraction
Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have different concepts. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What’s more, corpus about simile component extraction is limited. There are a number of rare words or unseen words, and the representations of these words are always not proper enough. Exiting models can hardly extract simile components accurately when there are low-frequency words in sentences. To solve these problems, we propose a hybrid representation-based component extraction (HRCE) model. Each word in HRCE is represented in three different levels: word level, concept level and character level. Concept representations (representations in concept level) can help HRCE to identify the words in cross-domain more accurately. Moreover, with the help of character representations (representations in character levels), HRCE can represent the meaning of a word more properly since words are consisted of characters and these characters can partly represent the meaning of words. We conduct experiments to compare the performance between HRCE and existing models. The experiment results show that HRCE significantly outperforms current models
ChatGPT vs State-of-the-Art Models: A Benchmarking Study in Keyphrase Generation Task
Transformer-based language models, including ChatGPT, have demonstrated
exceptional performance in various natural language generation tasks. However,
there has been limited research evaluating ChatGPT's keyphrase generation
ability, which involves identifying informative phrases that accurately reflect
a document's content. This study seeks to address this gap by comparing
ChatGPT's keyphrase generation performance with state-of-the-art models, while
also testing its potential as a solution for two significant challenges in the
field: domain adaptation and keyphrase generation from long documents. We
conducted experiments on six publicly available datasets from scientific
articles and news domains, analyzing performance on both short and long
documents. Our results show that ChatGPT outperforms current state-of-the-art
models in all tested datasets and environments, generating high-quality
keyphrases that adapt well to diverse domains and document lengths
Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction
Keyphrase extraction (KPE) is an important task in Natural Language
Processing for many scenarios, which aims to extract keyphrases that are
present in a given document. Many existing supervised methods treat KPE as
sequential labeling, span-level classification, or generative tasks. However,
these methods lack the ability to utilize keyphrase information, which may
result in biased results. In this study, we propose Diff-KPE, which leverages
the supervised Variational Information Bottleneck (VIB) to guide the text
diffusion process for generating enhanced keyphrase representations. Diff-KPE
first generates the desired keyphrase embeddings conditioned on the entire
document and then injects the generated keyphrase embeddings into each phrase
representation. A ranking network and VIB are then optimized together with rank
loss and classification loss, respectively. This design of Diff-KPE allows us
to rank each candidate phrase by utilizing both the information of keyphrases
and the document. Experiments show that Diff-KPE outperforms existing KPE
methods on a large open domain keyphrase extraction benchmark, OpenKP, and a
scientific domain dataset, KP20K.Comment: 10 pages, 2 figure
On Identifying Hashtags in Disaster Twitter Data
Tweet hashtags have the potential to improve the search for information
during disaster events. However, there is a large number of disaster-related
tweets that do not have any user-provided hashtags. Moreover, only a small
number of tweets that contain actionable hashtags are useful for disaster
response. To facilitate progress on automatic identification (or extraction) of
disaster hashtags for Twitter data, we construct a unique dataset of
disaster-related tweets annotated with hashtags useful for filtering actionable
information. Using this dataset, we further investigate Long Short Term
Memory-based models within a Multi-Task Learning framework. The best performing
model achieves an F1-score as high as 92.22%. The dataset, code, and other
resources are available on Github
EntropyRank: Unsupervised Keyphrase Extraction via Side-Information Optimization for Language Model-based Text Compression
We propose an unsupervised method to extract keywords and keyphrases from
texts based on a pre-trained language model (LM) and Shannon's information
maximization. Specifically, our method extracts phrases having the highest
conditional entropy under the LM. The resulting set of keyphrases turns out to
solve a relevant information-theoretic problem: if provided as side
information, it leads to the expected minimal binary code length in compressing
the text using the LM and an entropy encoder. Alternately, the resulting set is
an approximation via a causal LM to the set of phrases that minimize the
entropy of the text when conditioned upon it. Empirically, the method provides
results comparable to the most commonly used methods in various keyphrase
extraction benchmark challenges
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