Determining Semantic Textual Similarity using Natural Deduction Proofs (original) (raw)

Implementing Natural Language Inference for comparatives

Journal of Language Modelling

This paper presents a computational framework for Natural Language Inference (NLI) using logic-based semantic representations and theorem-proving. We focus on logical inferences with comparatives and other related constructions in English, which are known for their structural complexity and difficulty in performing efficient reasoning. Using the so-called A-not-A analysis of comparatives, we implement a fully automated system to map various comparative constructions to semantic representations in typed first-order logic via Combinatory Categorial Grammar parsers and to prove entailment relations via a theorem prover. We evaluate the system on a variety of NLI benchmarks that contain challenging inferences, in comparison with other recent logic-based systems and neural NLI models.

Sentence encoders for semantic textual similarity: A survey

The last decade has witnessed many accomplishments in the field of Natural Language Processing, especially in understanding the language semantics. Well-established machine learning models for generating word representation are available and has been proven useful. However, the existing techniques proposed for learning sentence level representations do not adequately capture the complexity of compositional semantics. Finding semantic similarity between sentences is a fundamental language understanding problem. In this project, we compare various machine learning models on their ability to capture the semantics of a sentence using Semantic Textual Similarity (STS) Task. We focus on models that exhibit state-of-the-art performance in Sem-Eval(2017) STS shared task. Also, we analyse the impact of models' internal architectures on STS task performance. Out of all the models the we compared, Bi-LSTM RNN with max-pooling layer achieves the best performance in extracting a generic semantic representation and aids in better transfer learning when compared to hierarchical CNN.

DLSITE-2: Semantic similarity based on syntactic dependency trees applied to textual entailment

2007

In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graphtheoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms.

Exploiting Syntactic and Semantic Information for Textual Similarity Estimation

Mathematical Problems in Engineering

The textual similarity task, which measures the similarity between two text pieces, has recently received much attention in the natural language processing (NLP) domain. However, due to the vagueness and diversity of language expression, only considering semantic or syntactic features, respectively, may cause the loss of critical textual knowledge. This paper proposes a new type of structure tree for sentence representation, which exploits both syntactic (structural) and semantic information known as the weight vector dependency tree (WVD-tree). WVD-tree comprises structure trees with syntactic information along with word vectors representing semantic information of the sentences. Further, Gaussian attention weight is proposed for better capturing important semantic features of sentences. Meanwhile, we design an enhanced tree kernel to calculate the common parts between two structures for similarity judgment. Finally, WVD-tree is tested on widely used semantic textual similarity tas...

Natural logic for textual inference

Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing - RTE '07, 2007

This paper presents the first use of a computational model of natural logic-a system of logical inference which operates over natural language-for textual inference. Most current approaches to the PAS-CAL RTE textual inference task achieve robustness by sacrificing semantic precision; while broadly effective, they are easily confounded by ubiquitous inferences involving monotonicity. At the other extreme, systems which rely on first-order logic and theorem proving are precise, but excessively brittle. This work aims at a middle way. Our system finds a low-cost edit sequence which transforms the premise into the hypothesis; learns to classify entailment relations across atomic edits; and composes atomic entailments into a top-level entailment judgment. We provide the first reported results for any system on the FraCaS test suite. We also evaluate on RTE3 data, and show that hybridizing an existing RTE system with our natural logic system yields significant performance gains.

UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016

We present our UWB system for Semantic Textual Similarity (STS) task at SemEval 2016. Given two sentences, the system estimates the degree of their semantic similarity. We use state-of-the-art algorithms for the meaning representation and combine them with the best performing approaches to STS from previous years. These methods benefit from various sources of information, such as lexical, syntactic, and semantic. In the monolingual task, our system achieve mean Pearson correlation 75.7% compared with human annotators. In the cross-lingual task, our system has correlation 86.3% and is ranked first among 26 systems.

Why do you say they are similar? Interpretable Semantic Textual Similarity

2016

User acceptance of artificial intelligence agents might depend on their ability to explain their reasoning, which requires adding an interpretability layer that facilitates users to understand their behavior. This paper focuses on adding an interpretable layer on top of Semantic Textual Similarity (STS), which measures the degree of semantic equivalence between two sentences. The interpretability layer is formalized as the alignment between pairs of segments across the two sentences, where the relation between the segments is labeled with a relation type and a similarity score. We present a publicly available dataset of sentence pairs annotated following the formalization. We then develop a system trained on this dataset which, given a sentence pair, explains what is similar and different, in the form of graded and typed segment alignments. When evaluated on the dataset, the system performs better than an informed baseline, showing that the dataset and task are well-defined and feas...

Methodology and Results for the Competition on Semantic Similarity Evaluation and Entailment Recognition for PROPOR 2016

ArXiv, 2017

In this paper, we present the methodology and the results obtained by our teams, dubbed Blue Man Group, in the ASSIN (from the Portuguese {\it Avalia\c{c}\~ao de Similaridade Sem\^antica e Infer\^encia Textual}) competition, held at PROPOR 2016\footnote{International Conference on the Computational Processing of the Portuguese Language - this http URL}. Our team's strategy consisted of evaluating methods based on semantic word vectors, following two distinct directions: 1) to make use of low-dimensional, compact, feature sets, and 2) deep learning-based strategies dealing with high-dimensional feature vectors. Evaluation results demonstrated that the first strategy was more promising, so that the results from the second strategy have been discarded. As a result, by considering the best run of each of the six teams, we have been able to achieve the best accuracy and F1 values in entailment recognition, in the Brazilian Portuguese set, and the best F1 score overall. In the semanti...

Word and tree-based similarities for textual entailment

2000

To calculate the textual entailment be- tween two sentences in the RTE data, we use a word-based similarity combined with a tree-based similarity approach. For each approach, we experiment with two different metrics. In order to combine the four different metrics we use an SVM trained on the development set. Our re- sults show an overall accuracy of 0.5437- 0.555.