68 research outputs found
Simple data-driven context-sensitive lemmatization
Lemmatization for languages with rich inflectional morphology is one of the basic, indispensable steps in a language processing pipeline. In this paper we present a simple data-driven context-sensitive approach to lemmatizating word forms in running text. We treat lemmatization as a classification task for Machine Learning, and automatically induce class labels. We achieve this by computing a Shortest Edit Script (SES) between reversed input and output strings. A SES describes the transformations that have to be applied to the
input string (word form) in order to convert it to the output string (lemma). Our approach shows competitive performance on a range of typologically different languages
Text segmentation with character-level text embeddings
Learning word representations has recently seen much success in computational
linguistics. However, assuming sequences of word tokens as input to linguistic
analysis is often unjustified. For many languages word segmentation is a
non-trivial task and naturally occurring text is sometimes a mixture of natural
language strings and other character data. We propose to learn text
representations directly from raw character sequences by training a Simple
recurrent Network to predict the next character in text. The network uses its
hidden layer to evolve abstract representations of the character sequences it
sees. To demonstrate the usefulness of the learned text embeddings, we use them
as features in a supervised character level text segmentation and labeling
task: recognizing spans of text containing programming language code. By using
the embeddings as features we are able to substantially improve over a baseline
which uses only surface character n-grams.Comment: Workshop on Deep Learning for Audio, Speech and Language Processing,
ICML 201
Symbolic inductive bias for visually grounded learning of spoken language
A widespread approach to processing spoken language is to first automatically
transcribe it into text. An alternative is to use an end-to-end approach:
recent works have proposed to learn semantic embeddings of spoken language from
images with spoken captions, without an intermediate transcription step. We
propose to use multitask learning to exploit existing transcribed speech within
the end-to-end setting. We describe a three-task architecture which combines
the objectives of matching spoken captions with corresponding images, speech
with text, and text with images. We show that the addition of the speech/text
task leads to substantial performance improvements on image retrieval when
compared to training the speech/image task in isolation. We conjecture that
this is due to a strong inductive bias transcribed speech provides to the
model, and offer supporting evidence for this.Comment: ACL 201
Correlating neural and symbolic representations of language
Analysis methods which enable us to better understand the representations and
functioning of neural models of language are increasingly needed as deep
learning becomes the dominant approach in NLP. Here we present two methods
based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which
allow us to directly quantify how strongly the information encoded in neural
activation patterns corresponds to information represented by symbolic
structures such as syntax trees. We first validate our methods on the case of a
simple synthetic language for arithmetic expressions with clearly defined
syntax and semantics, and show that they exhibit the expected pattern of
results. We then apply our methods to correlate neural representations of
English sentences with their constituency parse trees.Comment: ACL 201
Using machine-learning to assign function labels to parser output for Spanish
Data-driven grammatical function tag assignment has been studied for English using the Penn-II Treebank data. In this paper we address the question of whether such methods can be applied successfully to other languages and treebank resources. In addition to tag assignment accuracy
and f-scores we also present results of a task-based evaluation. We use three machine-learning methods to assign
Cast3LB function tags to sentences parsed with Bikel’s parser trained on the Cast3LB treebank. The best performing method, SVM, achieves an f-score of 86.87% on gold-standard trees and 66.67% on parser output - a statistically significant improvement of 6.74% over the baseline. In a
task-based evaluation we generate LFG functional-structures from the function tag-enriched trees. On this task we achive
an f-score of 75.67%, a statistically significant 3.4% improvement over the baseline
Improving treebank-based automatic LFG induction for Spanish
We describe several improvements to the method of treebank-based LFG induction for Spanish from the Cast3LB treebank (O’Donovan et al., 2005). We discuss the different categories of problems encountered and present the solutions adopted. Some of the problems involve a simple adoption of existing linguistic analyses, as in our treatment of clitic doubling and null subjects. In other cases there is no standard LFG account for the phenomenon
we wish to model and we adopt a compromise, conservative solution. This is exemplified by our treatment of Spanish periphrastic constructions. In yet another case, the less configurational nature of Spanish means that the LFG annotation algorithm has to rely mostly on Cast3LB function tags, and consequently a reliable method of adding those tags to parse trees had to be developed. This method achieves over 6% improvement over the baseline for the
Cast3LB-function-tag assignment task, and over 3% improvement over the baseline for LFG f-structure construction from function-tag-enriched trees
Towards a machine-learning architecture for lexical functional grammar parsing
Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also
recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages.
The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing.
In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able
to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously
lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages
Analyzing analytical methods: The case of phonology in neural models of spoken language
Given the fast development of analysis techniques for NLP and speech
processing systems, few systematic studies have been conducted to compare the
strengths and weaknesses of each method. As a step in this direction we study
the case of representations of phonology in neural network models of spoken
language. We use two commonly applied analytical techniques, diagnostic
classifiers and representational similarity analysis, to quantify to what
extent neural activation patterns encode phonemes and phoneme sequences. We
manipulate two factors that can affect the outcome of analysis. First, we
investigate the role of learning by comparing neural activations extracted from
trained versus randomly-initialized models. Second, we examine the temporal
scope of the activations by probing both local activations corresponding to a
few milliseconds of the speech signal, and global activations pooled over the
whole utterance. We conclude that reporting analysis results with randomly
initialized models is crucial, and that global-scope methods tend to yield more
consistent results and we recommend their use as a complement to local-scope
diagnostic methods.Comment: ACL 202
Using very large corpora to detect raising and control verbs
The distinction between raising and subject-control verbs, although crucial for the construction of semantics, is not easy to make given access to only the local syntactic configuration of the sentence. In most contexts raising verbs and control verbs display identical superficial syntactic structure. Linguists apply grammaticality tests to distinguish these verb classes. Our idea is to learn to predict the raising-control distinction by simulating such grammaticality judgments by means of pattern searches. Experiments with regression tree models show that using pattern counts from large unannotated corpora can be used to assess how likely a verb form is to appear in raising vs. control constructions. For this task it is beneficial to use the much larger but also noisier Web corpus rather than the smaller and cleaner Gigaword corpus. A similar methodology can be useful for detecting other lexical semantic distinctions: it could be used whenever a test employed to make linguistically interesting distinctions can be reduced to a pattern search in an unannotated
corpus
Learning language through pictures
We propose Imaginet, a model of learning visually grounded representations of
language from coupled textual and visual input. The model consists of two Gated
Recurrent Unit networks with shared word embeddings, and uses a multi-task
objective by receiving a textual description of a scene and trying to
concurrently predict its visual representation and the next word in the
sentence. Mimicking an important aspect of human language learning, it acquires
meaning representations for individual words from descriptions of visual
scenes. Moreover, it learns to effectively use sequential structure in semantic
interpretation of multi-word phrases.Comment: To appear at ACL 201
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