The parser is evalu- ated by converting its output into equivalent bracketing and improves on previously pub- lished results for unsupervised parsing from plain text. In this pa- per, we define the shared task and describe how the data sets were created. For one language pair, we observe a relative reduction in error of 53%. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. The technology, which has a variety of practical uses, is especially concerned with the methods, tools and software that can be used to parse automatically.
This book is the fourth in a line of such collections, and its breadth of coverage should make it suitable both as an overview of the state of the field for graduate students, and as a reference for established researchers in Computational Linguistics, Artificial Intelligence, Computer Science, Language Engineering, Information Science, and Cognitive Science. We test our tech- nique on part of speech tagging and show performance gains for varying amounts of source and target training data, as well as improvements in target domain parsing accuracy using our improved tagger. The proposed methods are applied to the parse reranking task, with various baseline models, achieving significant improvement both over the probabilistic models and the discriminative rerankers. Computer parsing technology, which breaks down complex linguistic structures into their constituent parts, is a key research area in the automatic processing of human language. This book is the fourth in a line of such collections, and its breadth of coverage should make it suitable both as an overview of the state of the field for graduate students, and as a reference for established researchers in Computational Linguistics, Artificial Intelligence, Computer Science, Language Engineering, Information Science, and Cognitive Science. Independent of the regularization, discriminative grammars significantly ou tperform their generative counterparts in our experiments.
This book collects contributions from leading researchers in the area of natural language processing technology, describing their recent work and a range of new techniques and results. The Conference on Computational Natu-ral Language Learning is accompanied ev-ery year by a shared task whose purpose is to promote natural language processing applications and evaluate them in a stan-dard setting. . In com-bination with other high recall systems it yields an F-measure of 81%. Secondly, we explore rule-based and learning tech-niques to extract predicate-argument struc-tures from this enriched output. Current Trends in Parsing Technology, Paola Merlo, Harry Bunt and Joakim Nivre Single Malt or Blended? Natural language processing has been highly influenced by deep learning and continuous representation of words known as word vectors. In particular, we study the task of morphological segmentation of multiple languages.
Significant advances in natural language processing require the development of adaptive syterns both for spoken and written language: systems that can interact naturally with human users, extend easily to new domains, produce readily usable translations of several languages, search the web rapidly and accurately, surnrnarise news coherently, and detect shifts in moods and emotions. Once the parame- ters of our model have been learned on bilin- gual parallel data, we evaluate its performance on a held-out monolingual test set. It is concerned with the decomposition of complex structures into their constituent parts, in particular with the methods, the tools and the software to parse automatically. Although trees have several desirable properties from both computational and linguistic perspectives, the structure of linguistic phenomena that goes beyond shallow syntax often cannot be fully captured by tree representations. The parser uses a representation for syntactic structure similar to dependency links which is well-suited for incremental parsing. We experimented with dependency trees converted from Penn treebank data, and achieved over 90% accuracy of word-word dependency. Thus, across domains, languages, and tree-bank annotations, a fundamental question arises: Is it possible to automatically induce an accurate parser from a tree-bank without resorting to full lexicalization? We make use of principal component analysis to extract the word vectors in an efficient way.
A compositional-semantics procedure is then used to map the augmented parse tree into a final meaning representation. This project aimed at extracting a set of interpretable word ve ctors from both raw and annotated corpora. Moreover, these improved trees yield a 2. The book presents a state-of-the-art overview of current research in parsing tehcnologies with a focus on three important themes in the field today: dependency parsing, domain adaptation, and deep parsing. New developments in the area of parsing technology are thus widely applicable. We apply our model to three Semitic languages: Arabic, He- brew, Aramaic, as well as to English. It will also be of interest to designers, developers, and advanced users of nautral language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.
The book presents an overview of the state of the art in current research into parsing technologies, focusing on three important themes: dependency parsing, domain adaptation, and deep parsing. The resulting parser is shown to be the best- performing system so far in a database query domain. On full-scale treebank parsing exper- iments, the discriminative latent models outperform both the comparable genera- tive latent models as well as the discriminative non-latent baselines. The resulting bitext parser outperforms state-of-the-art monolingual parser baselines by 2. This book collects contributions from leading researchers in the area of natural language processing technology, describing their recent work and a range of new techniques and results. Moreover, the trend is towards unsupervised rather than supervised learning methods due to the lack in most languages of annotated data and the applicability in wider domains Merlo et al. This is remarkable since training the parser and reranker on labeled Brown data achieves only 88.
This book is the fourth in a line of such collections, and its breadth of coverage should make it suitable both as an overview of the state of the field for graduate students, and as a reference for established researchers in Computational Linguistics, Artificial Intelligence, Computer Science, Language Engineering, Information Science, and Cognitive Science. This collection provides an excellent picture of the current state of affairs in this area. Parsing technology is a central area of research in the automatic processing of human language. The key hypothesis of multilin- gual learning is that by combining cues from multiple languages, the structure of each be- comes more apparent. We present a non- parametric Bayesian model that jointly in- duces morpheme segmentations of each lan- guage under consideration and at the same time identifies cross-lingual morpheme pat- terns, or abstract morphemes.
An evaluation of the method on translation from German to English shows similar performance to the phrase-based model of Koehn et al. To process non-planarity online, the semantic transition-based parser uses a new technique to dynamically reorder nodes during the derivation. For centuries, the deep connection between languages has brought about major discover- ies about human communication. Finally, we try to draw general conclusions about multi-lingual parsing: What makes a particular language, treebank or annotation scheme easier or harder to parse and which phenomena are challenging for any dependency parser? We propose a general method for reranker construction which targets choosing the candidate with the least expected loss, rather than the most probable candidate. In contrast to previous unsupervised parsers, the parser does not use part-of-speech tags and both learning and parsing are local and fast, requiring no explicit clustering or global optimization. It will also be of interest to designers, developers, and advanced users of natural language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology. With this new framework, we em- ploy a target dependency language model dur- ing decoding to exploit long distance word relations, which are unavailable with a tra- ditional n-gram language model.
Fur- thermore, we provide evidence that our joint model achieves better performance when ap- plied to languages from the same family. When restricted to sentences that are accepted by the parser, the degree of incrementality increases to 87. In this paper, we propose a novel string-to- dependency algorithm for statistical machine translation. Parsers are used in many application areas, such as information extraction from free text or speech, question answering, speech recognition and understanding, recommender systems, machine translation, and automatic summarization. This paper explores the idea that non-projective dependency parsing can be conceived as the outcome of two interleaved processes, one that sorts the words of a sentence into a canonical order, and one that performs strictly projective dependency parsing on the sorted input. This thoroughly reworked second edition includes revised and extended problems sets, updated analyses, additional examples and more detailed exposition throughout. This shared task not only unifies the shared tasks of the previous four years under a unique dependency-based formalism, but also ex-tends them significantly: this year's syn-tactic dependencies include more informa-tion such as named-entity boundaries; the semantic dependencies model roles of both verbal and nominal predicates.