234 research outputs found
Treebanks gone bad: generating a treebank of ungrammatical English
This paper describes how a treebank of ungrammatical
sentences can be created from a treebank of well-formed sentences. The treebank creation procedure involves the automatic introduction of frequently occurring grammatical errors into the sentences in an existing treebank, and the minimal transformation of the analyses in the treebank so
that they describe the newly created ill-formed sentences.
Such a treebank can be used to test how well a parser is able to ignore grammatical errors in texts (as people can), and can be used to induce a grammar capable of analysing such sentences. This paper also demonstrates the first of these uses
Corpus Annotation for Parser Evaluation
We describe a recently developed corpus annotation scheme for evaluating
parsers that avoids shortcomings of current methods. The scheme encodes
grammatical relations between heads and dependents, and has been used to mark
up a new public-domain corpus of naturally occurring English text. We show how
the corpus can be used to evaluate the accuracy of a robust parser, and relate
the corpus to extant resources.Comment: 7 pages, LaTeX (uses eaclap.sty
Universal, Unsupervised (Rule-Based), Uncovered Sentiment Analysis
We present a novel unsupervised approach for multilingual sentiment analysis
driven by compositional syntax-based rules. On the one hand, we exploit some of
the main advantages of unsupervised algorithms: (1) the interpretability of
their output, in contrast with most supervised models, which behave as a black
box and (2) their robustness across different corpora and domains. On the other
hand, by introducing the concept of compositional operations and exploiting
syntactic information in the form of universal dependencies, we tackle one of
their main drawbacks: their rigidity on data that are structured differently
depending on the language concerned. Experiments show an improvement both over
existing unsupervised methods, and over state-of-the-art supervised models when
evaluating outside their corpus of origin. Experiments also show how the same
compositional operations can be shared across languages. The system is
available at http://www.grupolys.org/software/UUUSA/Comment: 19 pages, 5 Tables, 6 Figures. This is the authors version of a work
that was accepted for publication in Knowledge-Based System
Real bad grammar: realistic grammatical description with grammaticality
Sampson (this issue) argues for a concept of “realistic grammatical description” in which the distinction between grammatical and ungrammatical sentences is irrelevant. In this article I also argue for a concept of “realistic grammatical description” but one in which a binary distinction between grammatical and ungrammatical sentences is maintained. In distinguishing between the grammatical and ungrammatical, this kind of grammar differs from that proposed by Sampson, but it does share the important property that invented sentences have no role to play, either as positive or negative evidence
Theoretical and pragmatic considerations on the lemmatization of non-standard Early Medieval Latin charters
This paper discusses the theoretical bases as well as the pragmatic implementation of the lemmatization of the Late Latin Charter Treebanks (LLCT). LLCT is a set of three dependency treebanks (LLCT1, LLCT2, LLCT3) of Early Medieval Latin documentary texts (charters) written in Italy between AD 714 and 1000 (c. 594,000 tokens). The original model for the lemmatization of LLCT was the Latin Dependency Treebank (LDT), which is mainly Classical standard Latin and based on the entries of Lewis and Short’s Latin Dictionary. Since LLCT reflects later linguistic developments of Latin and contains a plethora of non-standard proper names, particular attention is paid to how non-standard lexemes are lemmatized systematically to make the lemmatization maximally usable. The theoretical underpinnings to manage the lemmatization boil down to two principles: the evolutionary principle and the parsimony principle.Peer reviewe
Neural Combinatory Constituency Parsing
東京都立大学Tokyo Metropolitan University博士(情報科学)doctoral thesi
Improving Syntactic Parsing of Clinical Text Using Domain Knowledge
Syntactic parsing is one of the fundamental tasks of Natural Language Processing (NLP). However, few studies have explored syntactic parsing in the medical domain. This dissertation systematically investigated different methods to improve the performance of syntactic parsing of clinical text, including (1) Constructing two clinical treebanks of discharge summaries and progress notes by developing annotation guidelines that handle missing elements in clinical sentences; (2) Retraining four state-of-the-art parsers, including the Stanford parser, Berkeley parser, Charniak parser, and Bikel parser, using clinical treebanks, and comparing their performance to identify better parsing approaches; and (3) Developing new methods to reduce syntactic ambiguity caused by Prepositional Phrase (PP) attachment and coordination using semantic information.
Our evaluation showed that clinical treebanks greatly improved the performance of existing parsers. The Berkeley parser achieved the best F-1 score of 86.39% on the MiPACQ treebank. For PP attachment, our proposed methods improved the accuracies of PP attachment by 2.35% on the MiPACQ corpus and 1.77% on the I2b2 corpus. For coordination, our method achieved a precision of 94.9% and a precision of 90.3% for the MiPACQ and i2b2 corpus, respectively. To further demonstrate the effectiveness of the improved parsing approaches, we applied outputs of our parsers to two external NLP tasks: semantic role labeling and temporal relation extraction. The experimental results showed that performance of both tasks’ was improved by using the parse tree information from our optimized parsers, with an improvement of 3.26% in F-measure for semantic role labelling and an improvement of 1.5% in F-measure for temporal relation extraction
Judging grammaticality: experiments in sentence classification
A classifier which is capable of distinguishing a syntactically well formed sentence from a syntactically ill formed one has the potential to be useful in an L2 language-learning context. In this article, we describe a classifier which classifies English sentences as either well formed or ill formed using information gleaned from three different natural language processing techniques. We describe the issues involved in acquiring data to train such a classifier and present experimental results for this classifier on a variety of ill formed sentences. We demonstrate that (a) the combination of information from a variety of linguistic sources is helpful, (b) the trade-off between accuracy on well formed sentences and accuracy on ill formed sentences can be fine tuned by training multiple classifiers in a voting scheme, and (c) the performance of the classifier is varied, with better performance on transcribed spoken sentences produced by less advanced language learners
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