{ "paper_id": "M91-1031", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T03:15:23.151752Z" }, "title": "SYNCHRONETICS : DESCRIPTION OF THE SYNCHRONETICS SYSTE M USED FOR MUC-3", "authors": [ { "first": "James", "middle": [], "last": "Mayfield", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Edwin", "middle": [], "last": "Addison", "suffix": "", "affiliation": {}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Synchronetics, Inc ., is a startup company in Baltimore founded to develop text processing software product s for the commercial and Government sectors. The company, consisting of 7 people, was founded in 1989. Synchronetics had two natural language processing software development projects prior to participatio n in MUC-3 : an off-the-shelf parsing utility called NL-Builder ; and a text retrieval system prototype calle d Text-SR, which was developed under an SBIR contract for Wright Patterson Air Force Base. Neither of these projects alone was sufficient to handle the MUC-3 problem. Synchronetics was therefore prompted to look elsewhere for additional support. Members that participated on the `Synchronetics Team ' on a volunteer basis' were James Mayfield of the University of Maryland, Baltimore County (technical lea d and template generation software), Kenneth Litkowski of CL Research of Gaithersburg Md. (software for building the lexicon from a machine-readable dictionary), and Mark Wilson, Roy Cutts, and Bonnie Blade s (implementation of the semantic net and phrase and sentence interpretation) .", "pdf_parse": { "paper_id": "M91-1031", "_pdf_hash": "", "abstract": [ { "text": "Synchronetics, Inc ., is a startup company in Baltimore founded to develop text processing software product s for the commercial and Government sectors. The company, consisting of 7 people, was founded in 1989. Synchronetics had two natural language processing software development projects prior to participatio n in MUC-3 : an off-the-shelf parsing utility called NL-Builder ; and a text retrieval system prototype calle d Text-SR, which was developed under an SBIR contract for Wright Patterson Air Force Base. Neither of these projects alone was sufficient to handle the MUC-3 problem. Synchronetics was therefore prompted to look elsewhere for additional support. Members that participated on the `Synchronetics Team ' on a volunteer basis' were James Mayfield of the University of Maryland, Baltimore County (technical lea d and template generation software), Kenneth Litkowski of CL Research of Gaithersburg Md. (software for building the lexicon from a machine-readable dictionary), and Mark Wilson, Roy Cutts, and Bonnie Blade s (implementation of the semantic net and phrase and sentence interpretation) .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "The system was not integrated at the February meeting . At that time static cases were being passed b y hand from one processing stage to another . The complete system was fully integrated and running on 10 0 texts only three weeks before the final submission was due . Because of the relative youth of the system , little time was spent fine-tuning the algorithms and knowledge bases with the 1300 text development corpus . Therefore, we feel that the final results demonstrate the feasibility, but not the potential performance, o f our approach .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "We estimate that we spent 9 person-months on the development of our MUC-3 system, and that w e made use of about 9 person-months of work that was done before we initiated the project . The bulk of the latter time was spent in the development of the NL-Builder product, and in the development of a previou s LISP-based version of the KODIAK semantic net representation language . ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "The Synchronetics system architecture has been strongly influenced by the composition of the Synchronetic s team . With team members located at six different sites spread across Maryland, we needed an architectur e comprising components that could be developed separately and tested individually .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "ARCHITECTURE", "sec_num": null }, { "text": "The Synchronetics system consists of five 2 separate modules that communicate via a semantic net representation language in a pipelined fashion . Each module is a stand-alone program that is written in C an d operates on a variety of platforms . ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "ARCHITECTURE", "sec_num": null }, { "text": "A semantic net representation language (a variant of the KODIAK language) was developed for use with thi s project . World knowledge is represented as a single net that is made available to each of the components . I n addition, each component passes on to its successor a network description of the text, including all inference s that have been made about the text .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "A template generator", "sec_num": "5." }, { "text": "It was important to us both to maintain the pipelined architecture (to facilitate the development of differen t parts of the system at different sites), and to allow feedback from the semantic components of the syste m to the syntactic components . Therefore, we split the syntactic analysis component into two pieces : a phras e parser and a sentence parser. The phrase parser is responsible for breaking a text up into words, lookin g those words up in the dictionary, grouping the words into phrases, and constructing parse trees for thos e phrases . The sentence parser is a second parser that is responsible for constructing a single parse tree for eac h sentence in the message . The input to the sentence parser is a sequence of tokens representing the phrase s of a sentence as produced by the phrase interpreter . These processes are all performed by the Synchronetic s NL-Builder product .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "NL-Builder is a `programmable' parser . That is, the user may enter and modify the grammar, semantic interpretation rules and morphology, as well as import a dictionary . NL-Builder was used to provide bot h dictionary tools, and the two parsers . The significant components of NL-Builder are :", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "\u2022 DICTIONARY -The NL-Builder dictionary utilities include morphology rules that are modifiable b y the user, a B-tree compiler, and user-specifiable features on the lexical categories .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "Our initial dictionary was an available NL-Builder dictionary with 4000 words in it . It was not matche d to the domain, but it contained many common English words . This initial dictionary also included morphological rules, which were left largely unchanged . The dictionary was extended using utilities for dictionary building that are packaged with NL-Builder ; these utilities were run on the MUC-3 development corpus . This extension added many domain-specific terms and many slot fill terms and their synonyms . Ken Litkowski then built a system to extract information from the Proximity Linguistic System and enter it into the dictionary by comparing the dictionary with the words in th e MUC-3 test corpus . The linking of relevant word senses in the dictionary to the appropriate nodes o f the semantic network was done manually .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "The final dictionary consisted of approximately 10,000 word senses and about 30 morphological an d tokenization rules . The dictionary was compiled into a b-tree for fast access .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "\u2022 TOKENIZER -A tokenizer module (which comes as part of the NL-Builder system) is used for markin g text into tokens and identifying patterns that may not be in the dictionary (numbers, proper nouns , etc .) .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "\u2022 PARSER -The parser is an extended ATN . It allows a user-specified recursive network state definition with augmented conditions and actions on arcs . In addition, it allows look-ahead tests to prune searc h paths . Here is an example of a portion of the ATN that handles passive verbs : The parser produces a `syntactic net' that is stored in the same format as the semantic net . Here is a portion of the syntactic net that is produced by the phrase parser for the sentence (from message 9 9 of the tstl corpus) :", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "Some 3 years ago two Marines died following a Shining Path bombing of a market used b y Soviet Marines . '", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "Notice that the phrase parser has made a number of errors here, most notably the assumption tha t bombing ' is a verb : Semantic Interpreter s", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "The phrase interpreter is responsible for building a semantic interpretation of each of the phrases discovere d by the phrase parser . This process entails mapping from the words in the phrases to the corresponding node s in the semantic net, then attaching these nodes to each other according to the meaning of the phrase . The sentence interpreter is responsible for building a semantic interpretation of the entire sentence . It uses bot h the output of the phrase interpreter, and the output of the sentence parser .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "Our aim with the semantic interpreters was to make them robust enough to find appropriate connection s between the selected nodes in the semantic net even if no explicit semantic interpretation rules are availabl e for the syntactic structure being interpreted . Thus the basis for semantic interpretation is a spreadin g activation process . If there is a semantic interpretation rule for a given phrase, then that rule is used t o connect the nodes in the semantic net representing the components of the phrase . If, however, there is no semantic interpretation rule, spreading activation is used to find plausible connections between concepts .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "To continue our example, here is a portion of the phrase interpreter's output for the bombing sentence . Notice that the phrase interpreter has established mappings (via 'SI,' or Semantic Interpretation, links ) between the syntactic nodes produced by the phrase parser, and concept nodes in the semantic net : The sentence interpreter must put together an interpretation for the entire sentence . Here is a portion of it s output from this sentence : Notice that the sentence interpreter has identified the Shining Path organization as the perpetrator of th e bombing action .", "cite_spans": [ { "start": 165, "end": 211, "text": "(via 'SI,' or Semantic Interpretation, links )", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Parser s", "sec_num": null }, { "text": "The template generator is responsible for determining which actions that have been represented in th e semantic net should lead to the generation of a template, and for the creation of those templates . It begins b y examining each potentially reportable action in the semantic net (such as the children of KIDNAP_ACTION , the children of BOMB_ACTION, etc .) . For each such action, it tries to determine whether the action fall s within the parameters of a reportable action as laid out in the MUC-3 specifications . Since the long-term knowledge stored in the semantic net is currently quite limited, the system usually defaults to reporting th e action . Once an action to report has been selected, a template is created for the action, and its slots ar e filled one at a time . In most cases, slots are filled by starting from the node representing the action bein g reported, and following a path through the semantic net to another node that stands in the desired relatio n to the action node . Links are maintained from the syntactic world to the semantic world, so that the syste m can trace back from a node in the semantic net to the words that caused the creation of that node . For th e MUC-3 final test, we attempted to fill only slots 0-7 and slot 11 .", "cite_spans": [ { "start": 339, "end": 358, "text": "BOMB_ACTION, etc .)", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Template Generato r", "sec_num": null }, { "text": "Here is the template that is generated for the bombing sentence : The date of the incident was not extracted from the sentence, so an incorrect default (the date of the article ) was entered . Consequently, the bombing action met the date test, and the template was generated .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Template Generato r", "sec_num": null }, { "text": "'Synchronetics participation was funded for travel and incidental expenses only-all other labor was voluntary.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "A number of other components have been implemented or are under development, but were not included in the Phase 2 test .", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [], "bib_entries": {}, "ref_entries": { "FIGREF0": { "text": "Figure 1 : System Architecture", "uris": null, "num": null, "type_str": "figure" }, "FIGREF1": { "text": "Figure 1depicts this architecture . The five modules are :", "uris": null, "num": null, "type_str": "figure" } } } }