Papers with F-measure
Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text (D19-57)
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| Challenge: | Currently, the number of biomedical literature is growing at an exponential rate. |
| Approach: | They propose a Deep Learning architecture for pharmaceutical and chemical Named Entity Recognition in Spanish clinical cases texts. |
| Outcome: | The proposed model outperforms the state-of-the-art methods on the PharmaCoNER corpus . the proposed model is based on two bidirectional long-term memory and conditional random field networks . |
Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models (P18-3)
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| Challenge: | Encoder-decoder models employ words that are frequently used in the training corpus but may still include noisy words. |
| Approach: | They propose a method for selecting more suitable words for learning encoders by utilizing co-occurrence information. |
| Outcome: | The proposed method outperforms the baseline method in Japanese-to-English translation and grammatical error correction tasks with an F-measure of 1.48 points higher. |
Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level (L18-1)
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| Challenge: | Existing methods to detect dialogue act from utterances are limited in Arabic dialects . linguistic knowledge of the speaker is important for understanding spontaneous speech and instant messages . |
| Approach: | They propose a statistical dialogue analysis model to automatically recognize dialogue acts from a textual corpus. |
| Outcome: | The proposed model improves the F-measure by 20% . the proposed model can automatically acquire probabilistic discourse knowledge from a dialogue corpus . |
Content-Based Conflict of Interest Detection on Wikipedia (L18-1)
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| Challenge: | Conflict-of-Interest (CoI) editing is a problem on Wikipedia that is highly subjective . a key feature of Wiki sites is to allow people from all over the world to add or modify articles anonymously and without consequence. |
| Approach: | They frame CoI detection as a binary classification problem and explore features for it . they find that stylometric features outperform other types of features and give an F-measure of 0.63 . |
| Outcome: | The proposed method outperforms other features and gives an F-measure of 0.63 . the proposed method is not certain that the set of non-CoI articles contains any CoI articles . |
Open Event Extraction from Online Text using a Generative Adversarial Network (D19-1)
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| Challenge: | Existing approaches to extract structured representations of open-domain events are limited . a recent study shows that the model outperforms the baseline approaches for extracting events from online texts . |
| Approach: | They propose an event extraction model based on Generative Adversarial Nets which captures latent events with a generator network and a discriminator to distinguish documents reconstructed from latent and original events. |
| Outcome: | The proposed model outperforms baseline models on two Twitter and a news article datasets. |
Detecting Adverse Drug Reactions from Biomedical Texts with Neural Networks (P19-2)
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| Challenge: | Detection of adverse drug reactions in post-marketing period is a crucial challenge for pharmacology. |
| Approach: | They propose to use social media to extract information about adverse drug reactions . they compare four state-of-the-art attention-based neural networks to the F-measure . |
| Outcome: | The proposed methods perform better on four different benchmarks. |
Chinese Relation Classification using Long Short Term Memory Networks (L18-1)
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| Challenge: | Relation classification is the task to predict semantic relations between pairs of entities in a given text. |
| Approach: | They propose to extract relations between entities in Chinese text using a long-term memory network. |
| Outcome: | The proposed system achieves state-of-the-art F-measure on ACE 2005 corpus . it predicts relations between head entity e h and tail entity t from sentence . |
Adaptive Scaling for Sparse Detection in Information Extraction (P18-1)
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| Challenge: | Detection problems involving positive instances are often deficient in information extraction tasks . a number of researches have employed neural network models to solve detection problems . |
| Approach: | They propose an algorithm which can handle positive sparsity problem and directly optimize over F-measure . they borrow the idea of marginal utility from economics and propose a theoretical framework for instance importance measuring . |
| Outcome: | The proposed algorithm improves on positive sparsity problem and over F-measure . it leads to more effective and stable training of neural network based detection models. |
An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings (L18-1)
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| Challenge: | a study on the Algerian Arabic dialect aims to build a lexicon of words written in Arabic or Latin script . multilinguality of the corpus is due to the fact that people use several languages to post comments . stretched letters, misspelled words, emoticons, condensed writing are among the problems . |
| Approach: | They propose to build automatically from a social network an Algerian dialect lexicon. |
| Outcome: | The proposed method leads to a score of 73% on a test lexicon . the study is based on analyzing a lexical corpus of an Algerian dialect . |
FastSense: An Efficient Word Sense Disambiguation Classifier (L18-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a task that is often overlooked by NLP pipelines because of its complexity and complexity. |
| Approach: | They propose a neural network-based tool for word sense disambiguation called fastSense. |
| Outcome: | The proposed tool can process huge amounts of data quickly and surpasses state-of-the-art tools in terms of F-measure. |
Parsing Gapping Constructions Based on Grammatical and Semantic Roles (2020.emnlp-main)
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| Challenge: | Existing methods for parsing sentences with gapping recover elided elements from redundant elements . grammatical and semantic tags are used to identify gaps in a coordinated structure . |
| Approach: | They propose a method of parsing sentences with gapping to recover elided elements . they use constituent trees annotated with grammatical and semantic roles . |
| Outcome: | The proposed method outperforms the previous method in terms of F-measure and recall. |
If you’ve seen some, you’ve seen them all: Identifying variants of multiword expressions (C18-1)
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| Challenge: | Multiword expressions (VMWEs) show idiosyncratic variability, which is challenging for NLP applications. |
| Approach: | They propose to use a model to identify variants of previously seen VMWEs by comparing VMWAs with morpho-syntactic variations. |
| Outcome: | The proposed approach outperforms a baseline by 4 percent points of F-measure on a French corpus. |
Extraction of the Argument Structure of Tokyo Metropolitan Assembly Minutes: Segmentation of Question-and-Answer Sets (2020.lrec-1)
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Keiichi Takamaru, Yasutomo Kimura, Hideyuki Shibuki, Hokuto Ototake, Yuzu Uchida, Kotaro Sakamoto, Madoka Ishioroshi, Teruko Mitamura, Noriko Kando
| Challenge: | a study analyzed local assembly minutes in Japan using a unified format . local assembly minute data is expensive to analyze because of the different ways they are released to the public. |
| Approach: | They construct a corpus of Japanese local assembly minutes based on local autonomy law . they structured all statements in assembly minutes and extracted question and answer pairs . |
| Outcome: | The results show that the minutes are the primary information for local politics. |
Investigating Productive and Receptive Knowledge: A Profile for Second Language Learning (C18-1)
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| Challenge: | Literature on receptive and productive vocabulary often ignores grammar in second language acquisition studies. |
| Approach: | They use two corpora to investigate divergences in grammatical structures in texts . they set a polarity to the divergence scores to indicate whether there is overuse or underuse . |
| Outcome: | The proposed system will help language learners to activate more of their passive knowledge in writing texts. |
Toward a Task of Feedback Comment Generation for Writing Learning (D19-1)
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| Challenge: | Existing work on feedback comment generation has been limited . despite its usefulness, there is no publicly available dataset for research on feedback comments . |
| Approach: | They introduce a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning for non-native learners of English. |
| Outcome: | The proposed task is based on a corpus of 1,900 essays with all preposition errors annotated with feedback comments. |
Resource of Wikipedias in 31 Languages Categorized into Fine-Grained Named Entities (2022.coling-1)
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| Challenge: | a resource of Wikipedias in 31 languages is categorized into Extended Named Entity (ENE) ENE version 8 has 219 fine-grained NE categories. |
| Approach: | They describe a resource of Wikipedias in 31 languages categorized into Extended Named Entity (ENE) they first categorized 920 K Japanese Wikipedia pages using machine learning, then shared a task of Wikipedia categorization into 30 languages . |
| Outcome: | The proposed system is based on a dataset of Japanese Wikipedia pages . the dataset shows the best performance among the 30 languages . |
Humor Detection: A Transformer Gets the Last Laugh (D19-1)
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| Challenge: | Existing methods to identify humor in text have been limited to identifying humor in the text. |
| Approach: | They propose a model that learns to identify humorous jokes based on Reddit ratings, and employ a Transformer architecture to learn from sentence context. |
| Outcome: | The proposed model outperforms previous work on humor identification tasks with an F-measure of 93.1% for the Puns dataset and 98.6% on the Short Jokes dataset. |
Improving Hate Speech Detection with Deep Learning Ensembles (L18-1)
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| Challenge: | censorship is a potential risk when addressing these issues with automated text classification methods. |
| Approach: | They propose to use a neural network-based ensemble method to better classify hate speech using a publicly available embedding model and a popular sentiment dataset. |
| Outcome: | The proposed method improves by 5 points on a hate speech corpus from Twitter and a popular sentiment dataset. |
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis (D19-1)
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| Challenge: | Emotion cause analysis aims to identify the reasons behind emotions . previous models focus on learning architecture with local textual information . |
| Approach: | They propose a method to extract emotion cause with hierarchical neural model and knowledge-based regularizations by sentiment lexicon and common knowledge. |
| Outcome: | The proposed method outperforms baselines on two public datasets in different languages and outperformed competitive baselines by 2.08%. |
Improving Crowdsourcing-Based Annotation of Japanese Discourse Relations (L18-1)
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| Challenge: | Discourse parsing is an important task in natural language processing, but few languages have corpora annotated with discourse relations . crowdsourcing-based annotations are of poor quality and require expensive and time-consuming . et al. (2009) evaluated the quality of annotations using expert annotations. |
| Approach: | They construct a Japanese corpus with discourse annotations through crowdsourcing . they propose improvement techniques based on language tests . |
| Outcome: | The proposed methods improve the quality of the annotations, and will make them publicly available. |
Automatic Period Segmentation of Oral French (2020.lrec-1)
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| Challenge: | Analor is a semi-automatic tool for speech segmentation in periods but it only takes into account prosodic characteristics of speech. |
| Approach: | They propose to use a Fribourg model of macro-syntax to detect periods in syntactic and prosodic terms to develop an automatic tool for automatic segmentation of linguistic units. |
| Outcome: | The proposed tool is compared with an existing tool Analor which divides speech into smaller segments and that CRF models detect larger segments rather than macro-syntactic periods. |