1,651 research outputs found
Dialogue Act Recognition via CRF-Attentive Structured Network
Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from handcrafted feature extensions and attentive
contextual structural dependencies. In this paper, we consider the problem of
DAR from the viewpoint of extending richer Conditional Random Field (CRF)
structural dependencies without abandoning end-to-end training. We incorporate
hierarchical semantic inference with memory mechanism on the utterance
modeling. We then extend structured attention network to the linear-chain
conditional random field layer which takes into account both contextual
utterances and corresponding dialogue acts. The extensive experiments on two
major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder
Dialogue Act (MRDA) datasets show that our method achieves better performance
than other state-of-the-art solutions to the problem. It is a remarkable fact
that our method is nearly close to the human annotator's performance on SWDA
within 2% gap.Comment: 10 pages, 4figure
A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models
Word representation has always been an important research area in the history
of natural language processing (NLP). Understanding such complex text data is
imperative, given that it is rich in information and can be used widely across
various applications. In this survey, we explore different word representation
models and its power of expression, from the classical to modern-day
state-of-the-art word representation language models (LMS). We describe a
variety of text representation methods, and model designs have blossomed in the
context of NLP, including SOTA LMs. These models can transform large volumes of
text into effective vector representations capturing the same semantic
information. Further, such representations can be utilized by various machine
learning (ML) algorithms for a variety of NLP related tasks. In the end, this
survey briefly discusses the commonly used ML and DL based classifiers,
evaluation metrics and the applications of these word embeddings in different
NLP tasks
Joint Word Representation Learning Using a Corpus and a Semantic Lexicon.
Methods for learning word representations using large text corpora have received much attention lately due to their impressive performancein numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection.Despite their success, these data-driven word representation learning methods do not considerthe rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNetthat represent the meanings of words by defining the various relationships that exist among the words in a language.We consider the question, can we improve the word representations learnt using a corpora by integrating theknowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predictsthe co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon.We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus.Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into wordrepresentations on several benchmark datasets for semantic similarity and word analogy
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