Abstract I want to thank the ACL for this Lifetime Achievement Award. I am deeply honored to be receiving it. I would also like to thank the students, faculty, and researchers who were members of the Proteus Project during most of my professional lifetime. It was an honor to serve that group.

Relation schemas are often pre-defined for each relation dataset.Relation types can be related from different datasets and have overlapping semantics.We hypothesize we can combine these datasets according to the semantic relatedness between the relation types to overcome the problem of lack of training data.It is often easy to discover the connection between relation types based on relation names or annotation guides, but hard to measure the exact similarity and take advantage of the connection between the relation types from different datasets.We propose to use prototypical examples to represent each relation type and use these examples to augment related types from a different dataset.We obtain further improvement (ACE05) with this type augmentation over a strong baseline which uses multi-task learning between datasets to obtain better feature representation for relations.We make our implementation publicly available: https://github. com/fufrank5/relatedness

Abstract Information extraction is the process of converting unstructured text into a structured data base containing selected information from the text. It is an essential step in making the information content of the text usable for further processing. In this paper, we describe how information extraction has changed over the past 25 years, moving from hand-coded rules to neural networks, with a few stops on the way. We connect these changes to research advances in NLP and to the evaluations organized by the US Government.

The current neural network models for event detection have only considered the sequential representation of sentences. Syntactic representations have not been explored in this area although they provide an effective mechanism to directly link words to their informative context for event detection in the sentences. In this work, we investigate a convolutional neural network based on dependency trees to perform event detection. We propose a novel pooling method that relies on entity mentions to aggregate the convolution vectors. The extensive experiments demonstrate the benefits of the dependency-based convolutional neural networks and the entity mention-based pooling method for event detection. We achieve the state-of-the-art performance on widely used datasets with both perfect and predicted entity mentions.

Convolutional neural networks (CNN) have achieved the top performance for event detection due to their capacity to induce the underlying structures of the k-grams in the sentences.However, the current CNN-based event detectors only model the consecutive k-grams and ignore the non-consecutive kgrams that might involve important structures for event detection.In this work, we propose to improve the current CNN models for ED by introducing the non-consecutive convolution.Our systematic evaluation on both the general setting and the domain adaptation setting demonstrates the effectiveness of the nonconsecutive CNN model, leading to the significant performance improvement over the current state-of-the-art systems.

Event extraction is a particularly challenging problem in information extraction.The stateof-the-art models for this problem have either applied convolutional neural networks in a pipelined framework (Chen et al., 2015) or followed the joint architecture via structured prediction with rich local and global features (Li et al., 2013).The former is able to learn hidden feature representations automatically from data based on the continuous and generalized representations of words.The latter, on the other hand, is capable of mitigating the error propagation problem of the pipelined approach and exploiting the inter-dependencies between event triggers and argument roles via discrete structures.In this work, we propose to do event extraction in a joint framework with bidirectional recurrent neural networks, thereby benefiting from the advantages of the two models as well as addressing issues inherent in the existing approaches.We systematically investigate different memory features for the joint model and demonstrate that the proposed model achieves the state-of-the-art performance on the ACE 2005 dataset.

Thien Huu Nguyen, Barbara Plank, Ralph Grishman. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

We showcase ICE, an Integrated Customization Environment for Information Extraction. ICE is an easy tool for non-NLP experts to rapidly build customized IE systems for a new domain.

The task of Named Entity Disambiguation is to map entity mentions in the document to their correct entries in some knowledge base. We present a novel graph-based disambiguation approach based on Personalized PageRank (PPR) that combines local and global evidence for disambiguation and effectively filters out noise introduced by incorrect candidates. Experiments show that our method outperforms state-of-the-art approaches by achieving 91.7% in microand 89.9% in macroaccuracy on a dataset of 27.8K named entity mentions.

Relation extraction is the process of identifying instances of specified types of semantic relations in text; relation type extension involves extending a relation extraction system to recognize a new type of relation. We present LGCo-Testing, an active learning system for relation type extension based on local and global views of relation instances. Locally, we extract features from the sentence that contains the instance. Globally, we measure the distributional similarity between instances from a 2 billion token corpus. Evaluation on the ACE 2004 corpus shows that LGCo-Testing can reduce annotation cost by 97% while maintaining the performance level of supervised learning.

The Web brings an open-ended set of semantic relations. Discovering the significant types is very challenging. Unsupervised algorithms have been developed to extract relations from a corpus without knowing the relation types in advance, but most rely on tagging arguments of predefined types. One recently reported system is able to jointly extract relations and their argument semantic classes, taking a set of relation instances extracted by an open IE (Information Extraction) algorithm as input. However, it cannot handle polysemy of relation phrases and fails to group many similar (“synonymous”) relation instances because of the sparseness of features. In this paper, the authors present a novel unsupervised algorithm that provides a more general treatment of the polysemy and synonymy problems. The algorithm incorporates various knowledge sources which they will show to be very effective for unsupervised relation extraction. Moreover, it explicitly disambiguates polysemous relation phrases and groups synonymous ones. While maintaining approximately the same precision, the algorithm achieves significant improvement on recall compared to the previous method. It is also very efficient. Experiments on a real-world dataset show that it can handle 14.7 million relation instances and extract a very large set of relations from the Web.

We describe and analyze inference strategies for combining outputs from multiple question answering systems each of which was developed independently. Specifically, we address the DARPA-funded GALE information distillation Year 3 task of finding answers to the 5-Wh questions (who, what, when, where, and why) for each given sentence. The approach we take revolves around determining the best system using discriminative learning. In particular, we train support vector machines with a set of novel features that encode systems’ capabilities of returning as many correct answers as possible. We analyze two combination strategies: one combines multiple systems at the granularity of sentences, and the other at the granularity of individual fields. Our experimental results indicate that the proposed features and combination strategies were able to improve the overall performance by 22% to 36% relative to a random selection, 16% to 35% relative to a majority voting scheme, and 15% to 23% relative to the best individual system. Index Terms: Question answering, Systems for spoken language understanding

This paper studies the effect of automatic sentence boundary detection and comma prediction on entity and relation extraction in speech. We show that punctuating the machine generated transcript according to maximum F-measure of period and comma annotation results in suboptimal information extraction. Precisely, period and comma decision thresholds can be chosen in order to improve the entity value score and the relation value score by 4% relative. Error analysis shows that preventing noun-phrase splitting by generating longer sentences and fewer commas can be harmful for IE performance. Indeed, it seems that missed punctuation allows syntactic parsers to merge noun-phrases and prevent the extraction of correct information.

Cross-lingual Spoken Sentence Retrieval (CLSSR) remains a challenge, especially for queries including OOV words such as person names. This paper proposes a simple method of fuzzy matching between query names and phones of candidate audio segments. This approach has the advantage of avoiding some word decoding errors in Automatic Speech Recognition (ASR). Experiments on Mandarin-English CLSSR show that phone-based searching and conventional translation-based searching are complementary. Adding phone matching achieved 26.29% improvement on F-measure over searching on state-of-the-art Machine Translation (MT) output and 8.83% over Entity Translation (ET) output.

This work looks at the impact of automatically predicted commas on part-of-speech (POS) and name tagging of speech recognition transcripts of Mandarin broadcast news. There is a significant gain in both POS and name tagging accuracy due to using automatically predicted commas over sentence boundary prediction alone. One difference between Mandarin and English is that there are two types of commas, and experiments here show that, while they can be reliably distinguished in automatic prediction, the distinction does not give a clear benefit for POS or name tagging.

Information extraction systems incorporate multiple stages of linguistic analysis. Although errors are typically compounded from stage to stage, it is possible to reduce the errors in one stage by harnessing the results of the other stages. We demonstrate this by using the results of coreference analysis and relation extraction to reduce the errors produced by a Chinese name tagger. We use an N-best approach to generate multiple hypotheses and have them re-ranked by subsequent stages of processing. We obtained thereby a reduction of 24% in spurious and incorrect name tags, and a reduction of 14% in missed tags.

Entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. Information from three different levels of processing is considered: tokenization, sentence parsing and deep dependency analysis. Each source of information is represented by kernel functions. Then composite kernels are developed to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by information from other levels. We present an evaluation of these methods on the 2004 ACE relation detection task, using Support Vector Machines, and show that each level of syntactic processing contributes useful information for this task. When evaluated on the official test data, our approach produced very competitive ACE value scores. We also compare the SVM with KNN on different kernels.

Discovering the significant relations embedded in documents would be very useful not only for information retrieval but also for question answering and summarization. Prior methods for relation discovery, however, needed large annotated corpora which cost a great deal of time and effort. We propose an unsupervised method for relation discovery from large corpora. The key idea is clustering pairs of named entities according to the similarity of context words intervening between the named entities. Our experiments using one year of newspapers reveals not only that the relations among named entities could be detected with high recall and precision, but also that appropriate labels could be automatically provided for the relations.

Several approaches have been described for the automatic unsupervised acquisition of patterns for information extraction. Each approach is based on a particular model for the patterns to be acquired, such as a predicate-argument structure or a dependency chain. The effect of these alternative models has not been previously studied. In this paper, we compare the prior models and introduce a new model, the Subtree model, based on arbitrary subtrees of dependency trees. We describe a discovery procedure for this model and demonstrate experimentally an improvement in recall using Subtree patterns.

We developed a cross-lingual, question-answering (CLQA) system for Hindi and English. It accepts questions in English, finds candidate answers in Hindi newspapers, and translates the answer candidates into English along with the context surrounding each answer. The system was developed as part of the surprise language exercise (SLE) within the TIDES program.

The findings of a workshop, the goals of which were to identify applications, research problems, and designs of high performance computing and communications (HPCC) systems for supporting applications are discussed. In computer vision, the main scientific issues are machine learning, surface reconstruction, inverse optics and integration, model acquisition, and perception and action. In speech and natural language processing (SNLP), issues were identified statistical analysis in corpus-based speech and language understanding, search strategies for language analysis, auditory and vocal-tract modeling, integration of multiple levels of speech and language analyses, and connectionist systems. In AI, important issues that need immediate attention include the development of efficient machine learning and heuristic search methods that can adapt to different architectural configurations, and the design and construction of scalable and verifiable knowledge bases, active memories, and artificial neural networks.>

No abstract available.

Sublanguages differ from each other, and from the "standard language", in their syntactic, semantic, and discourse properties. Understanding these differnces is important if we are to improve our ability to process these sublanguages. We have developed a semiautomatic procedure for identifying sublanguage syntactic usage from a sample of text in the sublanguage. We describe the results of applying this procedure to three text samples: two sets of medical documents and a set of equipment failure messages.

Abstract Natural language processing has two primary roles to play in the storage and retrieval of large bodies of information: providing a friendly, easily‐learned interface to information retrieval systems, and automatically structuring texts so that their information can be more easily processed and retrieved. This article outlines the organization of a natural language interface for data retrieval (a “question—answering system”) and some of the approaches being taken to text structuring. It closes by describing a few of the research issues in computational linguistics and a possibility for using interactive natural language processing for information acquisition.

We present the design for the NYU Ultracomputer, a shared-memory MIMD parallel machine composed of thousands of autonomous processing elements. This machine uses an enhanced message switching network with the geometry of an Omega-network to approximate the ideal behavior of Schwartz's paracomputer model of computation and to implement efficiently the important fetch-and-add synchronization primitive. We outine the hardware that would be required to build a 4096 processor system using 1990's technology. We also discuss system software issues, and present analytic studies of the network performance. Finally, we include a sample of our effort to implement and simulate parallel variants of important scientific p̀rograms.

Fisher has recently described a new procedure for global microcode compaction which he calls "trace scheduling." We have implemented this procedure and tested it on several microcode sequences. We report in this correspondence on the relative effectiveness of local compaction, manual compaction, and trace scheduling on these sequences.

We present the design for the NYU Ultracomputer, a shared-memory MIMD parallel machine composed of thousands of autonomous processing elements. This machine uses an enhanced message switching network with the geometry of an Omega-network to approximate the ideal behavior of Schwartz's paracomputer model of computation and to implement efficiently the important fetch-and-add synchronization primitive. We outline the hardware that would be required to build a 4096 processor system using 1990's technology. We also discuss system software issues, and present analytic studies of the network performance. Finally, we include a sample of our effort to implement and simulate parallel variants of important scientific programs.

One reason for the wide variety of views on many subjects in computational linguistics (such as parsing) is the diversity of objectives which lead people to do research in this area. Some researchers are motivated primarily by potential applications - the development of natural language interfaces for computer systems. Others are primarily concerned with the psychological processes which underlie human language, and view the computer as a tool for modeling and thus improving our understanding of these processes. Since, as is often observed, man is our best example of a natural language processor, these two groups do have a strong commonality of research interest. Nonetheless, their divergence of objective must lead to differences in the way they regard the component processes of natural language understanding. (If - when human processing is better understood - it is recognized that the simulation of human processes is not the most effective way of constructing a natural language interface, there may even be a deliberate divergence in the processes themselves.) My work, and this position paper, reflect an applications orientation; those with different research objectives will come to quite different conclusions.

Article Free Access Share on Response generation in question answering systems Author: Ralph Grishman New York University New York UniversityView Profile Authors Info & Claims ACL '79: Proceedings of the 17th annual meeting on Association for Computational LinguisticsJune 1979 Pages 99–101https://doi.org/10.3115/982163.982192Online:29 June 1979Publication History 3citation231DownloadsMetricsTotal Citations3Total Downloads231Last 12 Months57Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF