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Named Entity Recognition for Hindi-English Code-Mixed Social Media Text 印地语-英语代码混合社交媒体文本的命名实体识别
NEWS@ACL Pub Date : 2018-07-01 DOI: 10.18653/v1/W18-2405
Vinay Singh, Deepanshu Vijay, S. S. Akhtar, Manish Shrivastava
{"title":"Named Entity Recognition for Hindi-English Code-Mixed Social Media Text","authors":"Vinay Singh, Deepanshu Vijay, S. S. Akhtar, Manish Shrivastava","doi":"10.18653/v1/W18-2405","DOIUrl":"https://doi.org/10.18653/v1/W18-2405","url":null,"abstract":"Named Entity Recognition (NER) is a major task in the field of Natural Language Processing (NLP), and also is a sub-task of Information Extraction. The challenge of NER for tweets lie in the insufficient information available in a tweet. There has been a significant amount of work done related to entity extraction, but only for resource rich languages and domains such as newswire. Entity extraction is, in general, a challenging task for such an informal text, and code-mixed text further complicates the process with it’s unstructured and incomplete information. We propose experiments with different machine learning classification algorithms with word, character and lexical features. The algorithms we experimented with are Decision tree, Long Short-Term Memory (LSTM), and Conditional Random Field (CRF). In this paper, we present a corpus for NER in Hindi-English Code-Mixed along with extensive experiments on our machine learning models which achieved the best f1-score of 0.95 with both CRF and LSTM.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134081205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 44
Connecting Distant Entities with Induction through Conditional Random Fields for Named Entity Recognition: Precursor-Induced CRF 通过条件随机场连接远程实体用于命名实体识别:前体诱导的CRF
NEWS@ACL Pub Date : 2018-05-26 DOI: 10.18653/v1/W18-2402
Wangjin Lee, Jinwook Choi
{"title":"Connecting Distant Entities with Induction through Conditional Random Fields for Named Entity Recognition: Precursor-Induced CRF","authors":"Wangjin Lee, Jinwook Choi","doi":"10.18653/v1/W18-2402","DOIUrl":"https://doi.org/10.18653/v1/W18-2402","url":null,"abstract":"This paper presents a method of designing specific high-order dependency factor on the linear chain conditional random fields (CRFs) for named entity recognition (NER). Named entities tend to be separated from each other by multiple outside tokens in a text, and thus the first-order CRF, as well as the second-order CRF, may innately lose transition information between distant named entities. The proposed design uses outside label in NER as a transmission medium of precedent entity information on the CRF. Then, empirical results apparently demonstrate that it is possible to exploit long-distance label dependency in the original first-order linear chain CRF structure upon NER while reducing computational loss rather than in the second-order CRF.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125588465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Tracing armed conflicts with diachronic word embedding models 用历时词嵌入模型追踪武装冲突
NEWS@ACL Pub Date : 2017-08-01 DOI: 10.18653/v1/W17-2705
Andrey Kutuzov, Erik Velldal, Lilja Øvrelid
{"title":"Tracing armed conflicts with diachronic word embedding models","authors":"Andrey Kutuzov, Erik Velldal, Lilja Øvrelid","doi":"10.18653/v1/W17-2705","DOIUrl":"https://doi.org/10.18653/v1/W17-2705","url":null,"abstract":"Recent studies have shown that word embedding models can be used to trace time-related (diachronic) semantic shifts in particular words. In this paper, we evaluate some of these approaches on the new task of predicting the dynamics of global armed conflicts on a year-to-year basis, using a dataset from the conflict research field as the gold standard and the Gigaword news corpus as the training data. The results show that much work still remains in extracting ‘cultural’ semantic shifts from diachronic word embedding models. At the same time, we present a new task complete with an evaluation set and introduce the ‘anchor words’ method which outperforms previous approaches on this set.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114055789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 32
The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction 事件故事线语料库:因果关系和时间关系提取的新基准
NEWS@ACL Pub Date : 2017-08-01 DOI: 10.18653/v1/W17-2711
Tommaso Caselli, P. Vossen
{"title":"The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction","authors":"Tommaso Caselli, P. Vossen","doi":"10.18653/v1/W17-2711","DOIUrl":"https://doi.org/10.18653/v1/W17-2711","url":null,"abstract":"This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark dataset for the temporal and causal relation detection. By developing this dataset, we also introduce a new task, the StoryLine Extraction from news data, which aims at extracting and classifying events relevant for stories, from across news documents spread in time and clustered around a single seminal event or topic. In addition to describing the dataset, we also report on three baselines systems whose results show the complexity of the task and suggest directions for the development of more robust systems.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127758930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 91
The Circumstantial Event Ontology (CEO) 情境事件本体(CEO)
NEWS@ACL Pub Date : 2017-08-01 DOI: 10.18653/v1/W17-2706
R. Segers, Tommaso Caselli, P. Vossen
{"title":"The Circumstantial Event Ontology (CEO)","authors":"R. Segers, Tommaso Caselli, P. Vossen","doi":"10.18653/v1/W17-2706","DOIUrl":"https://doi.org/10.18653/v1/W17-2706","url":null,"abstract":"In this paper we describe the ongoing work on the Circumstantial Event Ontology (CEO), a newly developed ontology for calamity events that models semantic circumstantial relations between event classes. The circumstantial relations are designed manually, based on the shared properties of each event class. We discuss and contrast two types of event circumstantial relations: semantic circumstantial relations and episodic circumstantial relations. Further, we show the metamodel and the current contents of the ontology and outline the evaluation of the CEO.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128414291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
On the Creation of a Security-Related Event Corpus 安全相关事件语料库的创建研究
NEWS@ACL Pub Date : 2017-08-01 DOI: 10.18653/v1/W17-2709
M. Atkinson, J. Piskorski, Hristo Tanev, Vanni Zavarella
{"title":"On the Creation of a Security-Related Event Corpus","authors":"M. Atkinson, J. Piskorski, Hristo Tanev, Vanni Zavarella","doi":"10.18653/v1/W17-2709","DOIUrl":"https://doi.org/10.18653/v1/W17-2709","url":null,"abstract":"This paper reports on an effort of creating a corpus of structured information on security-related events automatically extracted from on-line news, part of which has been manually curated. The main motivation behind this effort is to provide material to the NLP community working on event extraction that could be used both for training and evaluation purposes.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125891900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Event Detection and Semantic Storytelling: Generating a Travelogue from a large Collection of Personal Letters 事件检测与语义叙事:从大量私人信件中生成游记
NEWS@ACL Pub Date : 2017-08-01 DOI: 10.18653/v1/W17-2707
Georg Rehm, J. Schneider, Peter Bourgonje, Ankit Srivastava, Jan Nehring, Armin Berger, Luca König, Sören Räuchle, Jens Gerth
{"title":"Event Detection and Semantic Storytelling: Generating a Travelogue from a large Collection of Personal Letters","authors":"Georg Rehm, J. Schneider, Peter Bourgonje, Ankit Srivastava, Jan Nehring, Armin Berger, Luca König, Sören Räuchle, Jens Gerth","doi":"10.18653/v1/W17-2707","DOIUrl":"https://doi.org/10.18653/v1/W17-2707","url":null,"abstract":"We present an approach at identifying a specific class of events, movement action events (MAEs), in a data set that consists of ca. 2,800 personal letters exchanged by the German architect Erich Mendelsohn and his wife, Luise. A backend system uses these and other semantic analysis results as input for an authoring environment that digital curators can use to produce new pieces of digital content. In our example case, the human expert will receive recommendations from the system with the goal of putting together a travelogue, i.e., a description of the trips and journeys undertaken by the couple. We describe the components and architecture and also apply the system to news data.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134032393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags 使用标签的时态聚类检测Twitter流中的变化
NEWS@ACL Pub Date : 2017-08-01 DOI: 10.18653/v1/W17-2702
Yunli Wang, Cyril Goutte
{"title":"Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags","authors":"Yunli Wang, Cyril Goutte","doi":"10.18653/v1/W17-2702","DOIUrl":"https://doi.org/10.18653/v1/W17-2702","url":null,"abstract":"Detecting events from social media data has important applications in public security, political issues, and public health. Many studies have focused on detecting specific or unspecific events from Twitter streams. However, not much attention has been paid to detecting changes, and their impact, in online conversations related to an event. We propose methods for detecting such changes, using clustering of temporal profiles of hashtags, and three change point detection algorithms. The methods were tested on two Twitter datasets: one covering the 2014 Ottawa shooting event, and one covering the Sochi winter Olympics. We compare our approach to a baseline consisting of detecting change from raw counts in the conversation. We show that our method produces large gains in change detection accuracy on both datasets.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116552395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
The Rich Event Ontology 丰富事件本体
NEWS@ACL Pub Date : 2017-08-01 DOI: 10.18653/v1/W17-2712
S. Brown, Claire Bonial, L. Obrst, Martha Palmer
{"title":"The Rich Event Ontology","authors":"S. Brown, Claire Bonial, L. Obrst, Martha Palmer","doi":"10.18653/v1/W17-2712","DOIUrl":"https://doi.org/10.18653/v1/W17-2712","url":null,"abstract":"In this paper we describe a new lexical semantic resource, The Rich Event On-tology, which provides an independent conceptual backbone to unify existing semantic role labeling (SRL) schemas and augment them with event-to-event causal and temporal relations. By unifying the FrameNet, VerbNet, Automatic Content Extraction, and Rich Entities, Relations and Events resources, the ontology serves as a shared hub for the disparate annotation schemas and therefore enables the combination of SRL training data into a larger, more diverse corpus. By adding temporal and causal relational information not found in any of the independent resources, the ontology facilitates reasoning on and across documents, revealing relationships between events that come together in temporal and causal chains to build more complex scenarios. We envision the open resource serving as a valuable tool for both moving from the ontology to text to query for event types and scenarios of interest, and for moving from text to the ontology to access interpretations of events using the combined semantic information housed there.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124673044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 24
Event Detection Using Frame-Semantic Parser 使用框架语义解析器进行事件检测
NEWS@ACL Pub Date : 2017-08-01 DOI: 10.18653/v1/W17-2703
Evangelia Spiliopoulou, E. Hovy, T. Mitamura
{"title":"Event Detection Using Frame-Semantic Parser","authors":"Evangelia Spiliopoulou, E. Hovy, T. Mitamura","doi":"10.18653/v1/W17-2703","DOIUrl":"https://doi.org/10.18653/v1/W17-2703","url":null,"abstract":"Recent methods for Event Detection focus on Deep Learning for automatic feature generation and feature ranking. However, most of those approaches fail to exploit rich semantic information, which results in relatively poor recall. This paper is a small & focused contribution, where we introduce an Event Detection and classification system, based on deep semantic information retrieved from a frame-semantic parser. Our experiments show that our system achieves higher recall than state-of-the-art systems. Further, we claim that enhancing our system with deep learning techniques like feature ranking can achieve even better results, as it can benefit from both approaches.","PeriodicalId":189654,"journal":{"name":"NEWS@ACL","volume":"15 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130802951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
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