Mary P. Harper

101 papers receiving 1.5k citations

Peers

Mary P. Harper
Comparison fields: 5 of 95
  • Artificial Intelligence 1.4k
  • Signal Processing 272
  • Experimental and Cognitive Psychology 231
  • Computer Vision and Pattern Recognition 180
  • Language and Linguistics 76
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Citations per field
00.5×4.7×
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Citations per year

Countries citing papers authored by Mary P. Harper

Since Specialization
Citations

This map shows the geographic impact of Mary P. Harper's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mary P. Harper with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mary P. Harper more than expected).

Fields of papers citing papers by Mary P. Harper

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mary P. Harper. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mary P. Harper. The network helps show where Mary P. Harper may publish in the future.

Co-authorship network of co-authors of Mary P. Harper

This figure shows the co-authorship network connecting the top 25 collaborators of Mary P. Harper. A scholar is included among the top collaborators of Mary P. Harper based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mary P. Harper. Mary P. Harper is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Learning from 26 Languages: Program Management and Science in the Babel Program
3
2
Generalized Interpolation in Decision Tree LM
0
3
Feature-Rich Log-Linear Lexical Model for Latent Variable PCFG Grammars
6
4
Non-Expert Correction of Automatically Generated Relation Annotations
11
5 25
6 1
7
Mandarin Part-of-Speech Tagging and Discriminative Reranking
31
8
An Open Source Prosodic Feature Extraction Tool
25
9
Linguistic Resources for Speech Parsing
3
10 27
11 27
12
Evaluating Factors Impacting the Accuracy of Forced Alignments in a Multimodal Corpus.
8
13 7
14 33
15
Analysis of Unknown Lexical Items using Morphological and Syntactic Information with the TIMIT Corpus.
2
16
Stroing logical form in a shared-packed forest
2
17
An approach to multiply segmented constraint satisfaction problems
3
18
Log Time Parsing on the MasPar MP-1.
12
19 6
20
Representing pronouns in logical form: computational constraints and linguistic evidence
2

About Mary P. Harper

Mary P. Harper is a scholar working on Artificial Intelligence, Signal Processing and Human-Computer Interaction, having authored 107 papers that have together received 1.7k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (71 papers), Topic Modeling (44 papers) and Speech Recognition and Synthesis (38 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Signal Processing (272 citations) and Experimental and Cognitive Psychology (231 citations). Mary P. Harper has collaborated with scholars based in United States, Germany and Australia. Frequent co-authors include Andreas Stolcke, Zhongqiang Huang, Yang Liu, Elizabeth Shriberg, Siripong Potisuk, Mari Ostendorf, Dustin Hillard, Jack Gandour, E. Shriberg and Leah H. Jamieson. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and The Journal of the Acoustical Society of America.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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