954 research outputs found
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Deep compositional models of meaning acting on distributional representations
of words in order to produce vectors of larger text constituents are evolving
to a popular area of NLP research. We detail a compositional distributional
framework based on a rich form of word embeddings that aims at facilitating the
interactions between words in the context of a sentence. Embeddings and
composition layers are jointly learned against a generic objective that
enhances the vectors with syntactic information from the surrounding context.
Furthermore, each word is associated with a number of senses, the most
plausible of which is selected dynamically during the composition process. We
evaluate the produced vectors qualitatively and quantitatively with positive
results. At the sentence level, the effectiveness of the framework is
demonstrated on the MSRPar task, for which we report results within the
state-of-the-art range.Comment: Accepted for presentation at EMNLP 201
Measuring Semantic Similarity: Representations and Methods
This dissertation investigates and proposes ways to quantify and measure semantic similarity between texts. The general approach is to rely on linguistic information at various levels, including lexical, lexico-semantic, and syntactic. The approach starts by mapping texts onto structured representations that include lexical, lexico-semantic, and syntactic information. The representation is then used as input to methods designed to measure the semantic similarity between texts based on the available linguistic information.While world knowledge is needed to properly assess semantic similarity of texts, in our approach world knowledge is not used, which is a weakness of it.We limit ourselves to answering the question of how successfully one can measure the semantic similarity of texts using just linguistic information.The lexical information in the original texts is retained by using the words in the corresponding representations of the texts. Syntactic information is encoded using dependency relations trees, which represent explicitly the syntactic relations between words. Word-level semantic information is relatively encoded through the use of semantic similarity measures like WordNet Similarity or explicitly encoded using vectorial representations such as Latent Semantic Analysis (LSA). Several methods are being studied to compare the representations, ranging from simple lexical overlap, to more complex methods such as comparing semantic representations in vector spaces as well as syntactic structures. Furthermore, a few powerful kernel models are proposed to use in combination with Support Vector Machine (SVM) classifiers for the case in which the semantic similarity problem is modeled as a classification task
A Deep Network Model for Paraphrase Detection in Short Text Messages
This paper is concerned with paraphrase detection. The ability to detect
similar sentences written in natural language is crucial for several
applications, such as text mining, text summarization, plagiarism detection,
authorship authentication and question answering. Given two sentences, the
objective is to detect whether they are semantically identical. An important
insight from this work is that existing paraphrase systems perform well when
applied on clean texts, but they do not necessarily deliver good performance
against noisy texts. Challenges with paraphrase detection on user generated
short texts, such as Twitter, include language irregularity and noise. To cope
with these challenges, we propose a novel deep neural network-based approach
that relies on coarse-grained sentence modeling using a convolutional neural
network and a long short-term memory model, combined with a specific
fine-grained word-level similarity matching model. Our experimental results
show that the proposed approach outperforms existing state-of-the-art
approaches on user-generated noisy social media data, such as Twitter texts,
and achieves highly competitive performance on a cleaner corpus
Semantic Representation and Inference for NLP
Semantic representation and inference is essential for Natural Language
Processing (NLP). The state of the art for semantic representation and
inference is deep learning, and particularly Recurrent Neural Networks (RNNs),
Convolutional Neural Networks (CNNs), and transformer Self-Attention models.
This thesis investigates the use of deep learning for novel semantic
representation and inference, and makes contributions in the following three
areas: creating training data, improving semantic representations and extending
inference learning. In terms of creating training data, we contribute the
largest publicly available dataset of real-life factual claims for the purpose
of automatic claim verification (MultiFC), and we present a novel inference
model composed of multi-scale CNNs with different kernel sizes that learn from
external sources to infer fact checking labels. In terms of improving semantic
representations, we contribute a novel model that captures non-compositional
semantic indicators. By definition, the meaning of a non-compositional phrase
cannot be inferred from the individual meanings of its composing words (e.g.,
hot dog). Motivated by this, we operationalize the compositionality of a phrase
contextually by enriching the phrase representation with external word
embeddings and knowledge graphs. Finally, in terms of inference learning, we
propose a series of novel deep learning architectures that improve inference by
using syntactic dependencies, by ensembling role guided attention heads,
incorporating gating layers, and concatenating multiple heads in novel and
effective ways. This thesis consists of seven publications (five published and
two under review).Comment: PhD thesis, the University of Copenhage
Capturing lexical variation in MT evaluation using automatically built sense-cluster inventories
The strict character of most of the existing Machine Translation (MT) evaluation metrics does not permit them to capture lexical variation in translation. However, a central
issue in MT evaluation is the high correlation that the metrics should have with human judgments of translation quality. In order to achieve a higher correlation, the identification of sense correspondences between the compared translations becomes really important. Given
that most metrics are looking for exact correspondences, the evaluation results are often misleading concerning translation quality. Apart from that, existing metrics do not permit one to make a conclusive estimation of the impact of Word Sense Disambiguation techniques into
MT systems. In this paper, we show how information acquired by an unsupervised semantic analysis method can be used to render MT evaluation more sensitive to lexical semantics. The sense inventories built by this data-driven method are incorporated into METEOR: they replace WordNet for evaluation in English and render METEOR’s synonymy module operable in French. The evaluation results demonstrate that the use of these inventories gives rise to an increase in the number of matches and the correlation with human judgments of translation quality, compared to precision-based metrics
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