Search Results for author:
Found 20 papers, 6 papers with code
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.
PRIDE: Predicting Relationships in Conversations
Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots.
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler
State-of-the-art intent classification (IC) and slot filling (SF) methods often rely on data-intensive deep learning models, limiting their practicality for industry applications.
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.
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.
KnowledgeNet: A Benchmark Dataset for Knowledge Base Population
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web.
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.
Discovering the Functions of Language in Online Forums
In this work, we revisit the functions of language proposed by linguist Roman Jakobson and we highlight their potential in analyzing online forum conversations.
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.
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.
KOI at SemEval-2018 Task 5: Building Knowledge Graph of Incidents
We present KOI (Knowledge of Incidents), a system that given news articles as input, builds a knowledge graph (KOI-KG) of incidental events.
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.
CATENA: CAusal and TEmporal relation extraction from NAtural language texts
The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts.
Ranked #1 on
Temporal Information Extraction
on TimeBank
On the contribution of word embeddings to temporal relation classification
Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities.
Extracting Temporal and Causal Relations between Events
Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events.
