930 research outputs found
A Continuously Growing Dataset of Sentential Paraphrases
A major challenge in paraphrase research is the lack of parallel corpora. In
this paper, we present a new method to collect large-scale sentential
paraphrases from Twitter by linking tweets through shared URLs. The main
advantage of our method is its simplicity, as it gets rid of the classifier or
human in the loop needed to select data before annotation and subsequent
application of paraphrase identification algorithms in the previous work. We
present the largest human-labeled paraphrase corpus to date of 51,524 sentence
pairs and the first cross-domain benchmarking for automatic paraphrase
identification. In addition, we show that more than 30,000 new sentential
paraphrases can be easily and continuously captured every month at ~70%
precision, and demonstrate their utility for downstream NLP tasks through
phrasal paraphrase extraction. We make our code and data freely available.Comment: 11 pages, accepted to EMNLP 201
Extending the adverbial coverage of a NLP oriented resource for French
This paper presents a work on extending the adverbial entries of LGLex: a NLP
oriented syntactic resource for French. Adverbs were extracted from the
Lexicon-Grammar tables of both simple adverbs ending in -ment '-ly' (Molinier
and Levrier, 2000) and compound adverbs (Gross, 1986; 1990). This work relies
on the exploitation of fine-grained linguistic information provided in existing
resources. Various features are encoded in both LG tables and they haven't been
exploited yet. They describe the relations of deleting, permuting, intensifying
and paraphrasing that associate, on the one hand, the simple and compound
adverbs and, on the other hand, different types of compound adverbs. The
resulting syntactic resource is manually evaluated and freely available under
the LGPL-LR license.Comment: Proceedings of the 5th International Joint Conference on Natural
Language Processing (IJCNLP'11), Chiang Mai : Thailand (2011
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
Following the major success of neural language models (LMs) such as BERT or
GPT-2 on a variety of language understanding tasks, recent work focused on
injecting (structured) knowledge from external resources into these models.
While on the one hand, joint pretraining (i.e., training from scratch, adding
objectives based on external knowledge to the primary LM objective) may be
prohibitively computationally expensive, post-hoc fine-tuning on external
knowledge, on the other hand, may lead to the catastrophic forgetting of
distributional knowledge. In this work, we investigate models for complementing
the distributional knowledge of BERT with conceptual knowledge from ConceptNet
and its corresponding Open Mind Common Sense (OMCS) corpus, respectively, using
adapter training. While overall results on the GLUE benchmark paint an
inconclusive picture, a deeper analysis reveals that our adapter-based models
substantially outperform BERT (up to 15-20 performance points) on inference
tasks that require the type of conceptual knowledge explicitly present in
ConceptNet and OMCS
Question Paraphrase Generation for Question Answering System
The queries to a practical Question Answering (QA) system range from keywords, phrases, badly written questions, and occasionally grammatically perfect questions. Among different kinds of question analysis approaches, the pattern matching works well in analyzing such queries. It is costly to build this pattern matching module because tremendous manual labor is needed to expand its coverage to so many variations in natural language questions. This thesis proposes that the costly manual labor should be saved by the technique of paraphrase generation which can automatically generate semantically similar paraphrases of a natural language question. Previous approaches of paraphrase generation either require large scale of corpus and the dependency parser, or only deal with the relation-entity type of simple question queries. By introducing a method of inferring transformation operations between paraphrases, and a description of sentence structure, this thesis develops a paraphrase generation method and its implementation in Chinese with very limited amount of corpus. The evaluation results of this implementation show its ability to aid humans to efficiently create a pattern matching module for QA systems as it greatly outperforms the human editors in the coverage of natural language questions, with an acceptable precision in generated paraphrases
Mining Social Science Publications for Survey Variables
Research in Social Science is usually based on survey data where individual research questions relate to observable concepts (variables). However, due to a lack of standards for data citations a reliable identification of the variables used is often difficult. In this paper, we present a work-in-progress study that seeks to provide a solution to the variable detection task based on supervised machine learning algorithms, using a linguistic analysis pipeline to extract a rich feature set, including terminological concepts and similarity metric scores. Further, we present preliminary results on a small dataset that has been specifically designed for this task, yielding modest improvements over the baseline
Learning Language from a Large (Unannotated) Corpus
A novel approach to the fully automated, unsupervised extraction of
dependency grammars and associated syntax-to-semantic-relationship mappings
from large text corpora is described. The suggested approach builds on the
authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well
as on a number of prior papers and approaches from the statistical language
learning literature. If successful, this approach would enable the mining of
all the information needed to power a natural language comprehension and
generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa
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