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Found 21 papers, 4 papers with code
Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended.
Rule Based Event Extraction for Artificial Social Intelligence
Natural language (as opposed to structured communication modes such as Morse code) is by far the most common mode of communication between humans, and can thus provide significant insight into both individual mental states and interpersonal dynamics.
A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction
We propose a system that assists a user in constructing transparent information extraction models, consisting of patterns (or rules) written in a declarative language, through program synthesis. Users of our system can specify their requirements through the use of examples, which are collected with a search interface. The rule-synthesis system proposes rule candidates and the results of applying them on a textual corpus; the user has the option to accept the candidate, request another option, or adjust the examples provided to the system. Through an interactive evaluation, we show that our approach generates high-precision rules even in a 1-shot setting.
Me, myself, and ire: Effects of automatic transcription quality on emotion, sarcasm, and personality detection
In deployment, systems that use speech as input must make use of automated transcriptions.
From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction
While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift.
An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification
This paper explores an unsupervised approach to learning a compositional representation function for multi-word expressions (MWEs), and evaluates it on the Tratz dataset, which associates two-word expressions with the semantic relation between the compound constituents (e. g. the label employer is associated with the noun compound government agency) (Tratz, 2011).
Towards the Necessity for Debiasing Natural Language Inference Datasets
Modeling natural language inference is a challenging task.
MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions
Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired.
AutoMATES: Automated Model Assembly from Text, Equations, and Software
Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations.
On the Importance of Delexicalization for Fact Verification
As expected, even though this method achieves high accuracy when evaluated in the same domain, the performance in the target domain is poor, marginally above chance. To mitigate this dependence on lexicalized information, we experiment with several strategies for masking out names by replacing them with their semantic category, coupled with a unique identifier to mark that the same or new entities are referenced between claim and evidence.
Semi-Supervised Teacher-Student Architecture for Relation Extraction
Generating a large amount of training data for information extraction (IE) is either costly (if annotations are created manually), or runs the risk of introducing noisy instances (if distant supervision is used).
Enabling Search and Collaborative Assembly of Causal Interactions Extracted from Multilingual and Multi-domain Free Text
Many of the most pressing current research problems (e. g., public health, food security, or climate change) require multi-disciplinary collaborations.
Eidos, INDRA, \& Delphi: From Free Text to Executable Causal Models
Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process.
A mostly unlexicalized model for recognizing textual entailment
For example, such a system trained in the news domain may learn that a sentence like {``}Palestinians recognize Texas as part of Mexico{''} tends to be unsupported, but this fact (and its corresponding lexicalized cues) have no value in, say, a scientific domain.
Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering
We also achieve 26. 56\% and 58. 36\% on ARC challenge and easy dataset respectively.
Deep Affix Features Improve Neural Named Entity Recognizers
We propose a practical model for named entity recognition (NER) that combines word and character-level information with a specific learned representation of the prefixes and suffixes of the word.
Tell Me Why: Using Question Answering as Distant Supervision for Answer Justification
We propose a neural network architecture for QA that reranks answer justifications as an intermediate (and human-interpretable) step in answer selection.
Ranked #1 on
Question Answering
on AI2 Kaggle Dataset
Framing QA as Building and Ranking Intersentence Answer Justifications
Our best configuration answers 44{\%} of the questions correctly, where the top justifications for 57{\%} of these correct answers contain a compelling human-readable justification that explains the inference required to arrive at the correct answer.
Creating Causal Embeddings for Question Answering with Minimal Supervision
We argue that a better approach is to look for answers that are related to the question in a relevant way, according to the information need of the question, which may be determined through task-specific embeddings.
