Papers by Gerhard Weikum
A Study of the Importance of External Knowledge in the Named Entity Recognition Task (P18-2)
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| Challenge: | Existing studies have shown that external knowledge is important for Named Entity Recognition . |
| Approach: | They propose a modular framework that divides knowledge into four categories according to depth . they show the effects when incrementally adding deeper knowledge . |
| Outcome: | The proposed framework outperforms agnostic frameworks with more external knowledge . the proposed frameworks outperformed agrarian frameworks on two standard datasets . |
Evaluating the Knowledge Base Completion Potential of GPT (2023.findings-emnlp)
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| Challenge: | Language models (LMs) have been proposed for unsupervised knowledge base completion (KBC) however, their ability to do this at scale and with high accuracy remains an open question. |
| Approach: | They propose to use language models to complete a large public KB, Wikidata, with 90% precision. |
| Outcome: | The proposed models can extend Wikidata by 27M facts at 90% precision. |
PRIDE: Predicting Relationships in Conversations (2021.emnlp-main)
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| Challenge: | Existing methods for extracting interpersonal relationships from dialogues are limited to end-to-end learning. |
| Approach: | They propose a neural multi-label classifier that infers relationships from dialogues by external knowledge about speaker features and conversation style. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on large-scale datasets with directed relationships of conversation participants. |
Facts That Matter (D18-1)
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| Challenge: | Existing methods to discover facts from natural language text are based on relation extraction and open information extraction. |
| Approach: | They propose a task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. |
| Outcome: | The proposed system outperforms baselines and text summarizers in a supervised evaluation of salience tasks. |
ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts (2020.emnlp-demos)
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| Challenge: | ENTYFI is a web-based system for fine-grained typing of entity mentions in fictional texts. |
| Approach: | They propose a web-based system for fine-grained typing of entity mentions in fictional texts . entity types are a core building block of current knowledge bases . |
| Outcome: | The proposed system builds on 205 automatically induced high-quality type systems for popular fictional domains and provides recommendations towards reference type systems. |
Recursive Question Understanding for Complex Question Answering over Heterogeneous Personal Data (2025.findings-acl)
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| Challenge: | a novel method for question answering over mixed sources, like text and tables, has been developed for question-answering . personal information is a prominent case of such heterogeneous data, such as calendar entries, workout statistics, shopping records, streaming history, and more. |
| Approach: | They propose a method that creates an executable operator tree for a given question . they use recursive decomposition to decompose a question into an operator tree . |
| Outcome: | The proposed method outperforms methods based on verbalization or translation . it can be executed on user devices and yields a traceable answer . |
AligNarr: Aligning Narratives on Movies (2021.acl-short)
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| Challenge: | Experimental results show the viability of an unsupervised approach to align movie scripts with plot summaries. |
| Approach: | They propose an unsupervised method to align movie scripts with plot summaries using a global optimization model. |
| Outcome: | The proposed method outperforms a baseline alignment model on ten movies with 76% F1 score. |
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora (P18-2)
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| Challenge: | Named Entity Disambiguation (NED) systems perform well on news articles but quality drops when inputs span long time periods. |
| Approach: | They propose a time-aware method that resolves ambiguities even when mention contexts give only few cues. |
| Outcome: | The proposed method improves on a newly created diachronic corpus. |
RedDust: a Large Reusable Dataset of Reddit User Traits (2020.lrec-1)
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| Challenge: | Social media is a rich source of assertions about personal traits, but identifying personal traits from implicit assertions is difficult because of the users’ highly varied vocabulary and expressions. |
| Approach: | They propose to build a large-scale annotated resource for user profiling for over 300k Reddit users across five attributes: profession, hobby, family status, age, and gender. |
| Outcome: | The proposed resource is the first annotated language resource about Reddit users at large scale. |
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (D18-1)
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| Challenge: | Recent work on automated fact-checking does not consider external evidence, but requires rich lexicons. |
| Approach: | They propose a neural network model that aggregates external evidence and language . they also derive informative features for generating user-comprehensible explanations . |
| Outcome: | The proposed model aggregates signals from external evidence articles, language and trustworthiness of their sources without human intervention. |
SANDI: Story-and-Images Alignment (2021.eacl-main)
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| Challenge: | a method for selecting images from an image collection and aligning them with text paragraphs of a story is presented . judiciously placed images are used for multimodal descriptions and narration in stories . |
| Approach: | They propose a method for automatically selecting images from an image collection and aligning them with text paragraphs of a story. |
| Outcome: | The proposed method can select and align images with texts with high quality of semantic fit. |
Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering (2021.acl-demo)
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| Challenge: | Structured knowledge bases (KBs) are a great way to explain and interpret outputs of systems leveraging the resources. |
| Approach: | They propose a web portal that allows users to understand its construction process and explore its content. |
| Outcome: | The proposed framework allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. |
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (N19-1)
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| Challenge: | ComQA dataset captures question phenomena and the diverse ways in which they are formulated. |
| Approach: | They propose a large dataset of real user questions that captures question phenomena and the diverse ways in which they are formulated. |
| Outcome: | The proposed dataset can be a driver of future research on factoid question answering (QA). |
CHARM: Inferring Personal Attributes from Conversations (2020.emnlp-main)
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| Challenge: | Personal Knowledge Bases (PKBs) capture individual user traits for customizing downstream applications like chatbots or recommenders. |
| Approach: | They propose a method that leverages keyword extraction and document retrieval to predict attribute values that were never seen during training. |
| Outcome: | The proposed method can predict attributes that were never seen during training. |
Coverage of Information Extraction from Sentences and Paragraphs (D19-1)
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| Challenge: | Textual information extraction (IE) uses textual features to negate stronger statements, such as the negation of stronger statements. |
| Approach: | They propose to use textual features to predict whether a given text segment mentions all objects standing in a certain relationship with a subject. |
| Outcome: | The proposed features can predict whether a given text segment mentions all objects standing in a certain relationship with a particular subject. |
Neural Relation Extraction for Knowledge Base Enrichment (P19-1)
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| Challenge: | Existing studies focus on the extraction itself and rely on Named Entity Disambiguation (NED) to map triples into knowledge base (KB) enrichment. |
| Approach: | They propose an end-to-end relation extraction model for knowledge base enrichment based on a neural encoder-decoder model . they propose to extract entities and their relationships from sentences in the form of triples and map the elements of the extracted triples to an existing KB in an end to end manner. |
| Outcome: | The proposed model outperforms state-of-the-art baselines by 15.51% and 8.38% on two real-world datasets. |
Predicting Document Coverage for Relation Extraction (2022.tacl-1)
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| Challenge: | Existing methods for predicting document coverage for relation extraction (RE) are limited in their predictive power. |
| Approach: | They propose a task of predicting the coverage of a text document for relation extraction . they analyze a dataset of 31,366 diverse documents for 520 entities . |
| Outcome: | The proposed model achieves an F1 score of up to 46% on two use cases. |
STANCY: Stance Classification Based on Consistency Cues (D19-1)
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| Challenge: | Recent work has shown that stance classification is a critical step for information credibility and automated fact-checking. |
| Approach: | They propose a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. |
| Outcome: | The proposed model outperforms existing methods on a Perspectrum dataset and shows that it is more accurate than existing methods. |