skip to main content

Explore Research Products in the NSF-PAR

Title: Which Evaluations Uncover Sense Representations that Actually Make Sense?
Text representations are critical for modern natural language processing. One form of text representation, sense-specific embeddings, reflect a word’s sense in a sentence better than single-prototype word embeddings tied to each type. However, existing sense representations are not uniformly better: although they work well for computer-centric evaluations, they fail for human-centric tasks like inspecting a language’s sense inventory. To expose this discrepancy, we propose a new coherence evaluation for sense embeddings. We also describe a minimal model (Gumbel Attention for Sense Induction) optimized for discovering interpretable sense representations that are more coherent than existing sense embeddings.  more » « less
Award ID(s):
1409287
NSF-PAR ID:
10212069
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of the 12th Language Resources and Evaluation Conference
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ( , ACM)
    Proceedings of the Sixteenth (Ed.)
    Instead of mining coherent topics from a given text corpus in a completely unsupervised manner, seed-guided topic discovery methods leverage user-provided seed words to extract distinctive and coherent topics so that the mined topics can better cater to the user’s interest. To model the semantic correlation between words and seeds for discovering topic-indicative terms, existing seedguided approaches utilize different types of context signals, such as document-level word co-occurrences, sliding window-based local contexts, and generic linguistic knowledge brought by pre-trained language models. In this work, we analyze and show empirically that each type of context information has its value and limitation in modeling word semantics under seed guidance, but combining three types of contexts (i.e., word embeddings learned from local contexts, pre-trained language model representations obtained from general-domain training, and topic-indicative sentences retrieved based on seed information) allows them to complement each other for discovering quality topics. We propose an iterative framework, SeedTopicMine, which jointly learns from the three types of contexts and gradually fuses their context signals via an ensemble ranking process. Under various sets of seeds and on multiple datasets, SeedTopicMine consistently yields more coherent and accurate topics than existing seed-guided topic discovery approaches. 
    more » « less
  2. ; ; ; ( , Proceedings of ACM Multimedia Conference)
    Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis.We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments. 
    more » « less
  3. ; ; ; ; ; ; ( , Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP))
    Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities. We propose a general method to embed multiple knowledge bases (KBs) into large scale models, and thereby enhance their representations with structured, human-curated knowledge. For each KB, we first use an integrated entity linker to retrieve relevant entity embeddings, then update contextual word representations via a form of word-to-entity attention. In contrast to previous approaches, the entity linkers and self-supervised language modeling objective are jointly trained end-to-end in a multitask setting that combines a small amount of entity linking supervision with a large amount of raw text. After integrating WordNet and a subset of Wikipedia into BERT, the knowledge enhanced BERT (KnowBert) demonstrates improved perplexity, ability to recall facts as measured in a probing task and downstream performance on relationship extraction, entity typing, and word sense disambiguation. KnowBert’s runtime is comparable to BERT’s and it scales to large KBs. 
    more » « less
  4. ; ( , Proceedings of the 43nd International ACM SIGIR Conference on Research & Development in Information Retrieval)
    Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations. Neural IR models have achieved promising results in learning query-document relevance patterns, but few explorations have been done on understanding the text content of a query or a document. This paper studies leveraging a recently-proposed contextual neural language model, BERT, to provide deeper text understanding for IR.Experimental results demonstrate that the contextual text representations from BERT are more effective than traditional word embeddings. Compared to bag-of-words retrieval models, the contextual language model can better leverage language structures, bringing large improvements on queries written in natural languages. Combining the text understanding ability with search knowledge leads to an enhanced pre-trained BERT model that can benefit related search tasks where training data are limited. 
    more » « less
  5. ; ( , ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)
    Abstract

    As inspirational stimuli can assist designers with achieving enhanced design outcomes, supporting the retrieval of impactful sources of inspiration is important. Existing methods facilitating this retrieval have relied mostly on semantic relationships, e.g., analogical distances. Increasingly, data-driven methods can be leveraged to represent diverse stimuli in terms of multi-modal information, enabling designers to access stimuli in terms of less explored, non-text-based relationships. Toward improved retrieval of multi-modal representations of inspirational stimuli, this work compares human-evaluated and computationally derived similarities between stimuli in terms of non-text-based visual and functional features. A human subjects study (n = 36) was conducted where similarity assessments between triplets of 3D-model parts were collected and used to construct psychological embedding spaces. Distances between unique part embeddings were used to represent similarities in terms of visual and functional features. Obtained distances were compared with computed distances between embeddings of the same stimuli generated using artificial intelligence (AI)-based deep-learning approaches. When used to assess similarity in appearance and function, these representations were found to be largely consistent, with highest agreement found when assessing pairs of stimuli with low similarity. Alignment between models was otherwise lower when identifying the same pairs of stimuli with higher levels of similarity. Importantly, qualitative data also revealed insights regarding how humans made similarity assessments, including more abstract information not captured using AI-based approaches. Toward providing inspiration to designers that considers design problems, ideas, and solutions in terms of non-text-based relationships, further exploration of how these relationships are represented and evaluated is encouraged.

     
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email