1,828 research outputs found
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling
Extracting knowledge from unlabeled texts using machine learning algorithms
can be complex. Document categorization and information retrieval are two
applications that may benefit from unsupervised learning (e.g., text clustering
and topic modeling), including exploratory data analysis. However, the
unsupervised learning paradigm poses reproducibility issues. The initialization
can lead to variability depending on the machine learning algorithm.
Furthermore, the distortions can be misleading when regarding cluster geometry.
Amongst the causes, the presence of outliers and anomalies can be a determining
factor. Despite the relevance of initialization and outlier issues for text
clustering and topic modeling, the authors did not find an in-depth analysis of
them. This survey provides a systematic literature review (2011-2022) of these
subareas and proposes a common terminology since similar procedures have
different terms. The authors describe research opportunities, trends, and open
issues. The appendices summarize the theoretical background of the text
vectorization, the factorization, and the clustering algorithms that are
directly or indirectly related to the reviewed works
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing
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