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Found 105 papers, 32 papers with code
Complex Temporal Question Answering on Knowledge Graphs
This work presents EXAQT, the first end-to-end system for answering complex temporal questions that have multiple entities and predicates, and associated temporal conditions.
Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs
We present a reinforcement learning model, termed CONQUER, that can learn from a conversational stream of questions and reformulations.
Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion
Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic.
Advanced Semantics for Commonsense Knowledge Extraction
Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O.
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Misinformation such as fake news is one of the big challenges of our society.
Commonsense Properties from Query Logs and Question Answering Forums
Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications.
Facts That Matter
This work introduces fact salience: The task of generating a machine-readable representation of the most prominent information in a text document as a set of facts.
Listening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation.
UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text
Some of these systems incorporate textual sources as additional evidence for the answering process, but cannot compute answers that are present in text alone.
Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases
Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates.
CROWN: Conversational Passage Ranking by Reasoning over Word Networks
Information needs around a topic cannot be satisfied in a single turn; users typically ask follow-up questions referring to the same theme and a system must be capable of understanding the conversational context of a request to retrieve correct answers.
PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems
Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust.
Conversational Question Answering over Passages by Leveraging Word Proximity Networks
In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns.
Refined Commonsense Knowledge from Large-Scale Web Contents
However, they are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and strings for P and O.
Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks
In conversational question answering, users express their information needs through a series of utterances with incomplete context.
ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI.
Extracting Cultural Commonsense Knowledge at Scale
Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledge projects.
Joint Reasoning for Multi-Faceted Commonsense Knowledge
Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid.
STANCY: Stance Classification Based on Consistency Cues
Controversial claims are abundant in online media and discussion forums.
Cross-Domain Learning for Classifying Propaganda in Online Contents
Prior work has focused on supervised learning with training data from the same domain.
Evaluating Language Models for Knowledge Base Completion
In a second step, we perform a human evaluation on predictions that are not yet in the KB, as only this provides real insights into the added value over existing KBs.
Enriching Knowledge Bases with Counting Quantifiers
In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2, 474 frequent relations in Wikidata.
KOGNAC: Efficient Encoding of Large Knowledge Graphs
We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judicious combination of statistical and semantic techniques.
AligNarr: Aligning Narratives on Movies
High-quality alignment between movie scripts and plot summaries is an asset for learning to summarize stories and to generate dialogues.
Answering Count Queries with Explanatory Evidence
A challenging case in web search and question answering are count queries, such as \textit{"number of songs by John Lennon"}.
Answering Count Questions with Structured Answers from Text
In this work we address the challenging case of answering count queries in web search, such as ``number of songs by John Lennon''.
Knowledge Base Completion for Long-Tail Entities
To evaluate our method and various baselines, we introduce a novel dataset, called MALT, rooted in Wikidata.
FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds
We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user.
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on a variety of real-world datasets.
KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition
KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge.
Multilingual Named Entity Recognition
named-entity-recognition
+2
Item Recommendation with Continuous Experience Evolution of Users using Brownian Motion
Online review communities are dynamic as users join and leave, adopt new vocabulary, and adapt to evolving trends.
Cardinal Virtues: Extracting Relation Cardinalities from Text
Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award.
People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities
This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources.
Credible Review Detection with Limited Information using Consistency Analysis
Online reviews provide viewpoints on the strengths and shortcomings of products/services, influencing potential customers' purchasing decisions.
People on Drugs: Credibility of User Statements in Health Communities
Online health communities are a valuable source of information for patients and physicians.
Item Recommendation with Evolving User Preferences and Experience
As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style.
Exploring Latent Semantic Factors to Find Useful Product Reviews
In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers.
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters
To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated.
J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features
Methods for Named Entity Recognition and Disambiguation (NERD) perform NER and NED in two separate stages.
A Study of the Importance of External Knowledge in the Named Entity Recognition Task
In this work, we discuss the importance of external knowledge for performing Named Entity Recognition (NER).
diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora
Named Entity Disambiguation (NED) systems perform well on news articles and other texts covering a specific time interval.
QUINT: Interpretable Question Answering over Knowledge Bases
We present QUINT, a live system for question answering over knowledge bases.
Efficiency-aware Answering of Compositional Questions using Answer Type Prediction
This paper investigates the problem of answering compositional factoid questions over knowledge bases (KB) under efficiency constraints.
TiFi: Taxonomy Induction for Fictional Domains [Extended version]
Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention.
Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment
Second, we reduce the computational cost by a new algorithm that embeds sample-based bisection, using spectral clustering or graph partitioning, in a hierarchical clustering process.
Operationalizing Individual Fairness with Pairwise Fair Representations
We revisit the notion of individual fairness proposed by Dwork et al. A central challenge in operationalizing their approach is the difficulty in eliciting a human specification of a similarity metric.
Neural Relation Extraction for Knowledge Base Enrichment
This way, NED errors may cause extraction errors that affect the overall precision and recall. To address this problem, we propose an end-to-end relation extraction model for KB enrichment based on a neural encoder-decoder model.
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA.
TEQUILA: Temporal Question Answering over Knowledge Bases
An important case, addressed here, is that of temporal questions, where cues for temporal relations need to be discovered and handled.
Story-oriented Image Selection and Placement
Multimodal contents have become commonplace on the Internet today, manifested as news articles, social media posts, and personal or business blog posts.
Know2Look: Commonsense Knowledge for Visual Search
With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web.
VISIR: Visual and Semantic Image Label Refinement
CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels.
Coverage of Information Extraction from Sentences and Paragraphs
Scalar implicatures are language features that imply the negation of stronger statements, e. g., {``}She was married twice{''} typically implicates that she was not married thrice.
Negative Statements Considered Useful
Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities.
Uncovering Hidden Semantics of Set Information in Knowledge Bases
This paper focuses on set-valued predicates, i. e., the relationship between an entity and a set of entities.
CounQER: A System for Discovering and Linking Count Information in Knowledge Bases
Predicate constraints of general-purpose knowledge bases (KBs) like Wikidata, DBpedia and Freebase are often limited to subproperty, domain and range constraints.
RedDust: a Large Reusable Dataset of Reddit User Traits
To the best of our knowledge, RedDust is the first annotated language resource about Reddit users at large scale.
Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases
Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI.
CHARM: Inferring Personal Attributes from Conversations
Personal knowledge about users{'} professions, hobbies, favorite food, and travel preferences, among others, is a valuable asset for individualized AI, such as recommenders or chatbots.
Robust Disambiguation of Named Entities in Text
Disambiguating named entities in naturallanguage text maps mentions of ambiguous names onto canonical entities like people or places, registered in a knowledge base such as DBpedia or YAGO.
Ranked #15 on
Entity Linking
on AIDA-CoNLL
Equity of Attention: Amortizing Individual Fairness in Rankings
We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program.
Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering
ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 2021).
SANDI: Story-and-Images Alignment
The Internet contains a multitude of social media posts and other of stories where text is interspersed with images.
ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts
Fiction and fantasy are archetypes of long-tail domains that lack suitable NLP methodologies and tools.
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning
Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI.
You Get What You Chat: Using Conversations to Personalize Search-based Recommendations
The paper develops an expressive model and effective methods for personalizing search-based entity recommendations.
Personalized Entity Search by Sparse and Scrutable User Profiles
Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests.
Language Models As or For Knowledge Bases
Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs).
Exploiting Image–Text Synergy for Contextual Image Captioning
Modern web content - news articles, blog posts, educational resources, marketing brochures - is predominantly multimodal.
PRIDE: Predicting Relationships in Conversations
Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots.
Enriching Knowledge Bases with Interesting Negative Statements
Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking.
KnowFi: Knowledge Extraction from Long Fictional Texts
Knowledge base construction has recently been extended to fictional domains like multi-volume novels and TV/movie series, aiming to support explorative queries for fans and sub-culture studies by humanities researchers.
Cultural Vocal Bursts Intensity Prediction
Relation Extraction
Predicting Document Coverage for Relation Extraction
We employ methods combining features with statistical models like TF-IDF and language models like BERT.
Conversational Question Answering on Heterogeneous Sources
Conversational question answering (ConvQA) tackles sequential information needs where contexts in follow-up questions are left implicit.
UnCommonSense: Informative Negative Knowledge about Everyday Concepts
This way, positive statements about comparable concepts that are absent for the target concept become seeds for negative statement candidates.
Class Cardinality Comparison as a Fermi Problem
Questions on class cardinality comparisons are quite tricky to answer and come with its own challenges.
CompMix: A Benchmark for Heterogeneous Question Answering
Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources.
Extracting Multi-valued Relations from Language Models
The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge.
Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation
This implies that training is limited to surface forms seen in the respective datasets, and evaluation is on a small set of held-out questions.
Evaluating the Knowledge Base Completion Potential of GPT
Structured knowledge bases (KBs) are an asset for search engines and other applications, but are inevitably incomplete.
Recommendations by Concise User Profiles from Review Text
Recommender systems are most successful for popular items and users with ample interactions (likes, ratings etc.).
Multi-Cultural Commonsense Knowledge Distillation
Despite recent progress, large language models (LLMs) still face the challenge of appropriately reacting to the intricacies of social and cultural conventions.
Faithful Temporal Question Answering over Heterogeneous Sources
The method has three stages: (i) understanding the question and its temporal conditions, (ii) retrieving evidence from all sources, and (iii) faithfully answering the question.
