6,279 research outputs found
The Cinderella Complex: Word Embeddings Reveal Gender Stereotypes in Movies and Books
Our analysis of thousands of movies and books reveals how these cultural
products weave stereotypical gender roles into morality tales and perpetuate
gender inequality through storytelling. Using the word embedding techniques, we
reveal the constructed emotional dependency of female characters on male
characters in stories
Semantic spaces
Any natural language can be considered as a tool for producing large
databases (consisting of texts, written, or discursive). This tool for its
description in turn requires other large databases (dictionaries, grammars
etc.). Nowadays, the notion of database is associated with computer processing
and computer memory. However, a natural language resides also in human brains
and functions in human communication, from interpersonal to intergenerational
one. We discuss in this survey/research paper mathematical, in particular
geometric, constructions, which help to bridge these two worlds. In particular,
in this paper we consider the Vector Space Model of semantics based on
frequency matrices, as used in Natural Language Processing. We investigate
underlying geometries, formulated in terms of Grassmannians, projective spaces,
and flag varieties. We formulate the relation between vector space models and
semantic spaces based on semic axes in terms of projectability of subvarieties
in Grassmannians and projective spaces. We interpret Latent Semantics as a
geometric flow on Grassmannians. We also discuss how to formulate G\"ardenfors'
notion of "meeting of minds" in our geometric setting.Comment: 32 pages, TeX, 1 eps figur
Beyond Sentiment: The Manifold of Human Emotions
Sentiment analysis predicts the presence of positive or negative emotions in
a text document. In this paper we consider higher dimensional extensions of the
sentiment concept, which represent a richer set of human emotions. Our approach
goes beyond previous work in that our model contains a continuous manifold
rather than a finite set of human emotions. We investigate the resulting model,
compare it to psychological observations, and explore its predictive
capabilities. Besides obtaining significant improvements over a baseline
without manifold, we are also able to visualize different notions of positive
sentiment in different domains.Comment: 15 pages, 7 figure
Program Synthesis using Natural Language
Interacting with computers is a ubiquitous activity for millions of people.
Repetitive or specialized tasks often require creation of small, often one-off,
programs. End-users struggle with learning and using the myriad of
domain-specific languages (DSLs) to effectively accomplish these tasks.
We present a general framework for constructing program synthesizers that
take natural language (NL) inputs and produce expressions in a target DSL. The
framework takes as input a DSL definition and training data consisting of
NL/DSL pairs. From these it constructs a synthesizer by learning optimal
weights and classifiers (using NLP features) that rank the outputs of a
keyword-programming based translation. We applied our framework to three
domains: repetitive text editing, an intelligent tutoring system, and flight
information queries. On 1200+ English descriptions, the respective synthesizers
rank the desired program as the top-1 and top-3 for 80% and 90% descriptions
respectively
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
We introduce a novel method for multilingual transfer that utilizes deep
contextual embeddings, pretrained in an unsupervised fashion. While contextual
embeddings have been shown to yield richer representations of meaning compared
to their static counterparts, aligning them poses a challenge due to their
dynamic nature. To this end, we construct context-independent variants of the
original monolingual spaces and utilize their mapping to derive an alignment
for the context-dependent spaces. This mapping readily supports processing of a
target language, improving transfer by context-aware embeddings. Our
experimental results demonstrate the effectiveness of this approach for
zero-shot and few-shot learning of dependency parsing. Specifically, our method
consistently outperforms the previous state-of-the-art on 6 tested languages,
yielding an improvement of 6.8 LAS points on average.Comment: NAACL 201
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