Alignment-guided chunking

We introduce an adaptable monolingual chunking approach–Alignment-Guided Chunking (AGC)–which makes use of knowledge of word alignments acquired from bilingual corpora. Our approach is motivated by the observation that a sentence should be chunked differently depending the foreseen end-tasks. For example, given the different requirements of translation into (say) French and German, it is inappropriate to chunk up an English string in exactly the same way as preparation for translation into one or other of these languages. We test our chunking approach on two language pairs: French–English and German–English, where these two bilingual corpora share the same English sentences. Two chunkers trained on French–English (FE-Chunker) and German–English(DE-Chunker ) respectively are used to perform chunking on the same English sentences. We construct two test sets, each suitable for French– English and German–English respectively. The performance of the two chunkers is evaluated on the appropriate test set and with one reference translation only, we report Fscores of 32.63% for the FE-Chunker and 40.41% for the DE-Chunker

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

The World Wide Web holds a wealth of information in the form of unstructured texts such as customer reviews for products, events and more. By extracting and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained. We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Challenge on Semantic Sentiment Analysis. Our proposed architecture divides the task in two subtasks: aspect term extraction and aspect-specific sentiment extraction. This approach is flexible in that it allows to address each subtask independently. As a first step, a recurrent neural network is used to extract aspects from a text by framing the problem as a sequence labeling task. In a second step, a recurrent network processes each extracted aspect with respect to its context and predicts a sentiment label. The system uses pretrained semantic word embedding features which we experimentally enhance with semantic knowledge extracted from WordNet. Further features extracted from SenticNet prove to be beneficial for the extraction of sentiment labels. As the best performing system in its category, our proposed system proves to be an effective approach for the Aspect-Based Sentiment Analysis

BaseNP Supersense Tagging for Japanese Texts

PACLIC 23 / City University of Hong Kong / 3-5 December 200

An Efficient Architecture for Predicting the Case of Characters using Sequence Models

The dearth of clean textual data often acts as a bottleneck in several natural language processing applications. The data available often lacks proper case (uppercase or lowercase) information. This often comes up when text is obtained from social media, messaging applications and other online platforms. This paper attempts to solve this problem by restoring the correct case of characters, commonly known as Truecasing. Doing so improves the accuracy of several processing tasks further down in the NLP pipeline. Our proposed architecture uses a combination of convolutional neural networks (CNN), bi-directional long short-term memory networks (LSTM) and conditional random fields (CRF), which work at a character level without any explicit feature engineering. In this study we compare our approach to previous statistical and deep learning based approaches. Our method shows an increment of 0.83 in F1 score over the current state of the art. Since truecasing acts as a preprocessing step in several applications, every increment in the F1 score leads to a significant improvement in the language processing tasks.Comment: to be published in IEEE ICSC 2020 proceeding

Indonesian Language Term Extraction using Multi-Task Neural Network

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two multilayer perceptron neural networks to perform Part-of-Speech (PoS) labeling and Noun Phrase Chunking. Our models were trained as a joint model to solve this problem. Our proposed method, with an f-score of 86.80%, can be considered a state-of-the-art algorithm for performing term extraction in the Indonesian Language using noun phrase chunking

Chinese text chunking using lexicalized HMMS

This paper presents a lexicalized HMM-based approach to Chinese text chunking. To tackle the problem of unknown words, we formalize Chinese text chunking as a tagging task on a sequence of known words. To do this, we employ the uniformly lexicalized HMMs and develop a lattice-based tagger to assign each known word a proper hybrid tag, which involves four types of information: word boundary, POS, chunk boundary and chunk type. In comparison with most previous approaches, our approach is able to integrate different features such as part-of-speech information, chunk-internal cues and contextual information for text chunking under the framework of HMMs. As a result, the performance of the system can be improved without losing its efficiency in training and tagging. Our preliminary experiments on the PolyU Shallow Treebank show that the use of lexicalization technique can substantially improve the performance of a HMM-based chunking system. © 2005 IEEE.published_or_final_versio

A comparison of cooking recipe named entities between Japanese and English

In this paper, we analyze the structural differences between the instructional text in Japanese and English cooking recipes. First, we constructed an English recipe corpus of 100 recipes, designed to be comparable to an existing Japanese recipe corpus. We annotated recipe named entities (r-NEs) in the English corpus according to guidelines previously defined for Japanese. We trained a state-of-art NE recognizer, PWNER, on the English r-NEs, and achieved very similar accuracy and coverage to previous results for the Japanese corpus, thus demonstrating the quality and consistency of the annotations. Second, we compared the r-NEs annotated in the Japanese and English corpora, and uncovered lexical, semantic, and underlying structural differences between Japanese and English recipes. We discuss reasons for these differences, which have significant implications for cross-language retrieval and automatic translation of recipes