Universal Dependencies Parsing for Colloquial Singaporean English

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.Comment: Accepted by ACL 201

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the-art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.Comment: To appear in EMNLP 201

Deep neural networks for identification of sentential relations

Natural language processing (NLP) is one of the most important technologies in the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate mostly in language: web search, advertisement, emails, customer service, language translation, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained exciting performance across a broad array of NLP tasks. These models can often be trained in an end-to-end paradigm without traditional, task-specific feature engineering. This dissertation focuses on a specific NLP task --- sentential relation identification. Successfully identifying the relations of two sentences can contribute greatly to some downstream NLP problems. For example, in open-domain question answering, if the system can recognize that a new question is a paraphrase of a previously observed question, the known answers can be returned directly, avoiding redundant reasoning. For another, it is also helpful to discover some latent knowledge, such as inferring ``the weather is good today'' from another description ``it is sunny today''. This dissertation presents some deep neural networks (DNNs) which are developed to handle this sentential relation identification problem. More specifically, this problem is addressed by this dissertation in the following three aspects. (i) Sentential relation representation is built on the matching between phrases of arbitrary lengths. Stacked Convolutional Neural Networks (CNNs) are employed to model the sentences, so that each filter can cover a local phrase, and filters in lower level span shorter phrases and filters in higher level span longer phrases. CNNs in stack enable to model sentence phrases in different granularity and different abstraction. (ii) Phrase matches contribute differently to the tasks. This motivates us to propose an attention mechanism in CNNs for these tasks, differing from the popular research of attention mechanisms in Recurrent Neural Networks (RNNs). Attention mechanisms are implemented in both convolution layer as well as pooling layer in deep CNNs, in order to figure out automatically which phrase of one sentence matches a specific phrase of the other sentence. These matches are supposed to be indicative to the final decision. Another contribution in terms of attention mechanism is inspired by the observation that some sentential relation identification task, like answer selection for multi-choice question answering, is mainly determined by phrase alignments of stronger degree; in contrast, some tasks such as textual entailment benefit more from the phrase alignments of weaker degree. This motivates us to propose a dynamic ``attentive pooling'' to select phrase alignments of different intensities for different task categories. (iii) In certain scenarios, sentential relation can only be successfully identified within specific background knowledge, such as the multi-choice question answering based on passage comprehension. In this case, the relation between two sentences (question and answer candidate) depends on not only the semantics in the two sentences, but also the information encoded in the given passage. Overall, the work in this dissertation models sentential relations in hierarchical DNNs, different attentions and different background knowledge. All systems got state-of-the-art performances in representative tasks.Die Verarbeitung natürlicher Sprachen (engl.: natural language processing - NLP) ist eine der wichtigsten Technologien des Informationszeitalters. Weiterhin ist das Verstehen komplexer sprachlicher Ausdrücke ein essentieller Teil künstlicher Intelligenz. Anwendungen von NLP sind überall zu finden, da Menschen haupt\-säch\-lich über Sprache kommunizieren: Internetsuchen, Werbung, E-Mails, Kundenservice, Übersetzungen, etc. Es gibt eine große Anzahl Tasks und Modelle des maschinellen Lernens für NLP-Anwendungen. In den letzten Jahren haben Deep-Learning-Ansätze vielversprechende Ergebnisse für eine große Anzahl verschiedener NLP-Tasks erzielt. Diese Modelle können oft end-to-end trainiert werden, kommen also ohne auf den Task zugeschnittene Feature aus. Diese Dissertation hat einen speziellen NLP-Task als Fokus: Sententielle Relationsidentifizierung. Die Beziehung zwischen zwei Sätzen erfolgreich zu erkennen, kann die Performanz für nachfolgende NLP-Probleme stark verbessern. Für open-domain question answering, zum Beispiel, kann ein System, das erkennt, dass eine neue Frage eine Paraphrase einer bereits gesehenen Frage ist, die be\-kann\-te Antwort direkt zurückgeben und damit mehrfaches Schlussfolgern vermeiden. Zudem ist es auch hilfreich, zu Grunde liegendes Wissen zu entdecken, so wie das Schließen der Tatsache "das Wetter ist gut" aus der Beschreibung "es ist heute sonnig". Diese Dissertation stellt einige tiefe neuronale Netze (eng.: deep neural networks - DNNs) vor, die speziell für das Problem der sententiellen Re\-la\-tions\-i\-den\-ti\-fi\-zie\-rung entwickelt wurden. Im Speziellen wird dieses Problem in dieser Dissertation unter den folgenden drei Aspekten behandelt: (i) Sententielle Relationsrepr\"{a}sentationen basieren auf einem Matching zwischen Phrasen beliebiger Länge. Tiefe convolutional neural networks (CNNs) werden verwendet, um diese Sätze zu modellieren, sodass jeder Filter eine lokale Phrase abdecken kann, wobei Filter in niedrigeren Schichten kürzere und Filter in höheren Schichten längere Phrasen umfassen. Tiefe CNNs machen es möglich, Sätze in unterschiedlichen Granularitäten und Abstraktionsleveln zu modellieren. (ii) Matches zwischen Phrasen tragen unterschiedlich zu unterschiedlichen Tasks bei. Das motiviert uns, einen Attention-Mechanismus für CNNs für diese Tasks einzuführen, der sich von dem bekannten Attention-Mechanismus für recurrent neural networks (RNNs) unterscheidet. Wir implementieren Attention-Mechanismen sowohl im convolution layer als auch im pooling layer tiefer CNNs, um herauszufinden, welche Phrasen eines Satzes bestimmten Phrasen eines anderen Satzes entsprechen. Wir erwarten, dass solche Matches die finale Entscheidung stark beeinflussen. Ein anderer Beitrag zu Attention-Mechanismen wurde von der Beobachtung inspiriert, dass einige sententielle Relationsidentifizierungstasks, zum Beispiel die Auswahl einer Antwort für multi-choice question answering hauptsächlich von Phrasen\-a\-lignie\-rungen stärkeren Grades bestimmt werden. Im Gegensatz dazu profitieren andere Tasks wie textuelles Schließen mehr von Phrasenalignierungen schwächeren Grades. Das motiviert uns, ein dynamisches "attentive pooling" zu entwickeln, um Phrasenalignierungen verschiedener Stärken für verschiedene Taskkategorien auszuwählen. (iii) In bestimmten Szenarien können sententielle Relationen nur mit entsprechendem Hintergrundwissen erfolgreich identifiziert werden, so wie multi-choice question answering auf der Grundlage des Verständnisses eines Absatzes. In diesem Fall hängt die Relation zwischen zwei Sätzen (der Frage und der möglichen Antwort) nicht nur von der Semantik der beiden Sätze, sondern auch von der in dem gegebenen Absatz enthaltenen Information ab. Insgesamt modellieren die in dieser Dissertation enthaltenen Arbeiten sententielle Relationen in hierarchischen DNNs, mit verschiedenen Attention-Me\-cha\-nis\-men und wenn unterschiedliches Hintergrundwissen zur Verf\ {u}gung steht. Alle Systeme erzielen state-of-the-art Ergebnisse für die entsprechenden Tasks

Statistical Deep parsing for spanish

This document presents the development of a statistical HPSG parser for Spanish. HPSG is a deep linguistic formalism that combines syntactic and semanticinformation in the same representation, and is capable of elegantly modelingmany linguistic phenomena. Our research consists in the following steps: design of the HPSG grammar, construction of the corpus, implementation of theparsing algorithms, and evaluation of the parsers performance. We created a simple yet powerful HPSG grammar for Spanish that modelsmorphosyntactic information of words, syntactic combinatorial valence, and semantic argument structures in its lexical entries. The grammar uses thirteenvery broad rules for attaching specifiers, complements, modifiers, clitics, relative clauses and punctuation symbols, and for modeling coordinations. In asimplification from standard HPSG, the only type of long range dependency wemodel is the relative clause that modifies a noun phrase, and we use semanticrole labeling as our semantic representation. We transformed the Spanish AnCora corpus using a semi-automatic processand analyzed it using our grammar implementation, creating a Spanish HPSGcorpus of 517,237 words in 17,328 sentences (all of AnCora). We implemented several statistical parsing algorithms and trained them overthis corpus. The implemented strategies are: a bottom-up baseline using bi-lexical comparisons or a multilayer perceptron; a CKY approach that uses theresults of a supertagger; and a top-down approach that encodes word sequencesusing a LSTM network. We evaluated the performance of the implemented parsers and compared them with each other and against other existing Spanish parsers. Our LSTM top-down approach seems to be the best performing parser over our test data, obtaining the highest scores (compared to our strategies and also to externalparsers) according to constituency metrics (87.57 unlabeled F1, 82.06 labeled F1), dependency metrics (91.32 UAS, 88.96 LAS), and SRL (87.68 unlabeled,80.66 labeled), but we must take in consideration that the comparison against the external parsers might be noisy due to the post-processing we needed to do in order to adapt them to our format. We also defined a set of metrics to evaluate the identification of some particular language phenomena, and the LSTM top-down parser out performed the baselines in almost all of these metrics as well.Este documento presenta el desarrollo de un parser HPSG estadístico para el español. HPSG es un formalismo lingüístico profundo que combina información sintáctica y semántica en sus representaciones, y es capaz de modelar elegantemente una buena cantidad de fenómenos lingüísticos. Nuestra investigación se compone de los siguiente pasos: diseño de la gramática HPSG, construcción del corpus, implementación de los algoritmos de parsing y evaluación de la performance de los parsers. Diseñamos una gramática HPSG para el español simple y a la vez poderosa, que modela en sus entradas léxicas la información morfosintáctica de las palabras, la valencia combinatoria sintáctica y la estructura argumental semántica. La gramática utiliza trece reglas genéricas para adjuntar especificadores, complementos, clíticos, cláusulas relativas y símbolos de puntuación, y también para modelar coordinaciones. Como simplificación de la teoría HPSG estándar, el único tipo de dependencia de largo alcance que modelamos son las cláusulas relativas que modifican sintagmas nominales, y utilizamos etiquetado de roles semánticos como representación semántica. Transformamos el corpus AnCora en español utilizando un proceso semiautomático y lo analizamos mediante nuestra implementación de la gramática, para crear un corpus HPSG en español de 517,237 palabras en 17,328 oraciones (todo el contenido de AnCora). Implementamos varios algoritmos de parsing estadístico entrenados sobre este corpus. En particular, teníamos como objetivo probar enfoques basados en redes neuronales. Las estrategias implementadas son: una línea base bottom-up que utiliza comparaciones bi-léxicas o un perceptrón multicapa; un enfoque tipo CKY que utiliza los resultados de un supertagger; y un enfoque top-down que codifica las secuencias de palabras mediante redes tipo LSTM. Evaluamos la performance de los parsers implementados y los comparamos entre sí y con un conjunto de parsers existententes para el español. Nuestro enfoque LSTM top-down parece ser el que tiene mejor desempeño para nuestro conjunto de test, obteniendo los mejores puntajes (comparado con nuestras estrategias y también con parsers externos) en cuanto a métricas de constituyentes (87.57 F1 no etiquetada, 82.06 F1 etiquetada), métricas de dependencias (91.32 UAS, 88.96 LAS), y SRL (87.68 no etiquetada, 80.66 etiquetada), pero debemos tener en cuenta que la comparación con parsers externos puede ser ruidosa debido al post procesamiento realizado para adaptarlos a nuestro formato. También definimos un conjunto de métricas para evaluar la identificación de algunos fenómenos particulares del lenguaje, y el parser LSTM top-down obtuvo mejores resultados que las baselines para casi todas estas métricas

Neural approaches to sequence labeling for information extraction

Een belangrijk aspect binnen artificiële intelligentie (AI) is het interpreteren van menselijke taal uitgedrukt in tekstuele (geschreven) vorm: natural Language processing (NLP) is belangrijk gezien tekstuele informatie nuttig is voor veel toepassingen. Toch is het verstaan ervan (zogenaamde natural Language understanding, (NLU) een uitdaging, gezien de ongestructureerde vorm van tekst, waarvan de betekenis vaak dubbelzinnig en contextafhankelijk is. In dit proefschrift introduceren we oplossingen voor tekortkomingen van gerelateerd werk bij het behandelen van fundamentele taken in natuurlijke taalverwerking, zoals named entity recognition (i.e. het identificeren van de entiteiten die in een zin voorkomen) en relatie-extractie (het identificeren van relaties tussen entiteiten). Vertrekkend van een specifiek probleem (met name het identificeren van de structuur van een huis aan de hand van een tekstueel zoekertje), bouwen we stapsgewijs een complete (geautomatiseerde) oplossing voor de bovengenoemde taken, op basis van neutrale netwerkarchitecturen. Onze oplossingen zijn algemeen toepasbaar op verschillende toepassingsdomeinen en talen. We beschouwen daarnaast ook de taak van het identificeren van relevante gebeurtenissen tijdens een evenement (bv. een doelpunt tijdens een voetbalwedstrijd), in informatiestromen op Twitter. Meer bepaald formuleren we dit probleem als het labelen van woord sequenties (vergelijkbaar met named entity recognition), waarbij we de chronologische relatie tussen opeenvolgende tweets benutten

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis

When learning a new skill, you take advantage of your preexisting skills and knowledge. For instance, if you are a skilled violinist, you will likely have an easier time learning to play cello. Similarly, when learning a new language you take advantage of the languages you already speak. For instance, if your native language is Norwegian and you decide to learn Dutch, the lexical overlap between these two languages will likely benefit your rate of language acquisition. This thesis deals with the intersection of learning multiple tasks and learning multiple languages in the context of Natural Language Processing (NLP), which can be defined as the study of computational processing of human language. Although these two types of learning may seem different on the surface, we will see that they share many similarities. The traditional approach in NLP is to consider a single task for a single language at a time. However, recent advances allow for broadening this approach, by considering data for multiple tasks and languages simultaneously. This is an important approach to explore further as the key to improving the reliability of NLP, especially for low-resource languages, is to take advantage of all relevant data whenever possible. In doing so, the hope is that in the long term, low-resource languages can benefit from the advances made in NLP which are currently to a large extent reserved for high-resource languages. This, in turn, may then have positive consequences for, e.g., language preservation, as speakers of minority languages will have a lower degree of pressure to using high-resource languages. In the short term, answering the specific research questions posed should be of use to NLP researchers working towards the same goal.Comment: PhD thesis, University of Groninge