Textual Entailment in Legal Bar Exam Question Answering Using Deep Siamese Networks

2018 ◽  
pp. 35-48
Author(s):  
Mi-Young Kim ◽  
Yao Lu ◽  
Randy Goebel
Keyword(s):  
Question Answering ◽  
Textual Entailment ◽  
Siamese Networks

EVENT AND TEMPORAL EXPRESSION EXTRACTION FROM RAW TEXT: FIRST STEP TOWARDS A TEMPORALLY AWARE SYSTEM

2010 ◽  
Vol 04 (04) ◽  
pp. 487-508 ◽  
Author(s):  
NAUSHAD UZZAMAN ◽  
JAMES F. ALLEN
Keyword(s):  
Question Answering ◽  
Conditional Random Field ◽  
Temporal Structure ◽  
Semantic Parsing ◽  
Temporal Expression ◽  
Markov Logic ◽  
Textual Entailment ◽  
Temporal Expressions ◽  
Markov Logic Network

Extracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. Our long-term goal is to build complete temporal structure of documents and use the temporal structure in other applications like textual entailment, question answering, visualization, or others. In this paper, we present a first step, a system for extracting events, event features, main events, temporal expressions and their normalized values from raw text. Our system is a combination of deep semantic parsing with extraction rules, Markov Logic Network classifiers and Conditional Random Field classifiers. To compare with existing systems, we evaluated our system on the TempEval-1 and TempEval-2 corpus. Our system outperforms or performs competitively with existing systems that evaluate on the TimeBank, TempEval-1 and TempEval-2 corpus and our performance is very close to inter-annotator agreement of the TimeBank annotators.

scholarly journals Natural language inference for Malayalam language using language agnostic sentence representation

2021 ◽  
Vol 7 ◽  
pp. e508
Author(s):  
Sara Renjit ◽  
Sumam Idicula
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Binary Classification ◽  
Multiclass Classification ◽  
The Other ◽  
Indian Language ◽  
Information Retrieval Systems ◽  
Textual Entailment ◽  
Malayalam Language

Natural language inference (NLI) is an essential subtask in many natural language processing applications. It is a directional relationship from premise to hypothesis. A pair of texts is defined as entailed if a text infers its meaning from the other text. The NLI is also known as textual entailment recognition, and it recognizes entailed and contradictory sentences in various NLP systems like Question Answering, Summarization and Information retrieval systems. This paper describes the NLI problem attempted for a low resource Indian language Malayalam, the regional language of Kerala. More than 30 million people speak this language. The paper is about the Malayalam NLI dataset, named MaNLI dataset, and its application of NLI in Malayalam language using different models, namely Doc2Vec (paragraph vector), fastText, BERT (Bidirectional Encoder Representation from Transformers), and LASER (Language Agnostic Sentence Representation). Our work attempts NLI in two ways, as binary classification and as multiclass classification. For both the classifications, LASER outperformed the other techniques. For multiclass classification, NLI using LASER based sentence embedding technique outperformed the other techniques by a significant margin of 12% accuracy. There was also an accuracy improvement of 9% for LASER based NLI system for binary classification over the other techniques.

Exploiting the Wikipedia structure in local and global classification of taxonomic relations

2012 ◽  
Vol 18 (2) ◽  
pp. 235-262 ◽  
Author(s):  
QUANG XUAN DO ◽  
DAN ROTH
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
General Purpose ◽  
Knowledge Sources ◽  
Knowledge Resources ◽  
General Terms ◽  
Taxonomic Relation ◽  
Textual Entailment ◽  
Global Classification ◽  
Low Coverage

AbstractDetermining whether two terms have an ancestor relation (e.g. Toyota Camry and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in Natural Language Processing applications such as Question Answering, Summarization, and Textual Entailment. Significant work has been done on developing knowledge sources that could support these tasks, but these resources usually suffer from low coverage, noise, and are inflexible when dealing with ambiguous and general terms that may not appear in any stationary resource, making their use as general purpose background knowledge resources difficult. In this paper, rather than building a hierarchical structure of concepts and relations, we describe an algorithmic approach that, given two terms, determines the taxonomic relation between them using a machine learning-based approach that makes use of existing resources. Moreover, we develop a global constraint-based inference process that leverages an existing knowledge base to enforce relational constraints among terms and thus improves the classifier predictions. Our experimental evaluation shows that our approach significantly outperforms other systems built upon the existing well-known knowledge sources.

Semantic Methods for Textual Entailment

2012 ◽  
pp. 479-494
Author(s):  
Andrew Neel ◽  
Max H. Garzon
Keyword(s):  
Question Answering ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
World Knowledge ◽  
New Approach ◽  
Sense Disambiguation ◽  
Textual Entailment ◽  
Benchmark Datasets ◽  
Previous Solution ◽  
Recognizing Textual Entailment

The problem of recognizing textual entailment (RTE) has been recently addressed using syntactic and lexical models with some success. Here, a new approach is taken to apply world knowledge in much the same way as humans, but captured in large semantic graphs such as WordNet. We show that semantic graphs made of synsets and selected relationships between them enable fairly simple methods that provide very competitive performance. First, assuming a solution to word sense disambiguation, we report on the performance of these methods in four basic areas: information retrieval (IR), information extraction (IE), question answering (QA), and multi-document summarization (SUM), as described using benchmark datasets designed to test the entailment problem in the 2006 Recognizing Textual Entailment (RTE-2) challenge. We then show how the same methods yield a solution to word sense disambiguation, which combined with the previous solution, yields a fully automated solution with about the same performance. We then evaluate this solution on two subsequent RTE Challenge datasets. Finally, we evaluate the contribution of WordNet to provide world knowledge. We conclude that the protocol itself works well at solving entailment given a quality source of world knowledge, but WordNet is not able to provide enough information to resolve entailment with this inclusion protocol.

scholarly journals Exploring Knowledge Graphs in an Interpretable Composite Approach for Text Entailment

2019 ◽  
Vol 33 ◽  
pp. 7023-7030
Author(s):  
Vivian S. Silva ◽  
André Freitas ◽  
Siegfried Handschuh
Keyword(s):  
Question Answering ◽  
Text Summarization ◽  
World Knowledge ◽  
Semantic Relationship ◽  
Syntactic Variation ◽  
Semantic Relationships ◽  
Composite Approach ◽  
Textual Entailment ◽  
Knowledge Graphs ◽  
Recognizing Textual Entailment

Recognizing textual entailment is a key task for many semantic applications, such as Question Answering, Text Summarization, and Information Extraction, among others. Entailment scenarios can range from a simple syntactic variation to more complex semantic relationships between pieces of text, but most approaches try a one-size-fits-all solution that usually favors some scenario to the detriment of another. We propose a composite approach for recognizing text entailment which analyzes the entailment pair to decide whether it must be resolved syntactically or semantically. We also make the answer interpretable: whenever an entailment is solved semantically, we explore a knowledge base composed of structured lexical definitions to generate natural language humanlike justifications, explaining the semantic relationship holding between the pieces of text. Besides outperforming wellestablished entailment algorithms, our composite approach gives an important step towards Explainable AI, using world knowledge to make the semantic reasoning process explicit and understandable.

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