LALR Research Articles

SURVEY PAPER ON REAL TIME SIGN LANGUAGE INTERPRETER

Deaf and Dumb people use sign language to communicate. There are various sign recognition techniques that produce output in the form of words or identified signs. The suggested method focuses on Sign Language Interpretation in correct sentences .In addition to sign identification several NLP (Natural Language Processing) techniques are applied .Input is given as video of sign language followed by framing and segmentation on video. Deaf people get isolated as result of this. However, if an android application can be developed to convert sign language into written and audio format, the gap between normal people and the deaf community can be narrowed. The HAAR CASCADE classifier is used to identify signs. The continuous words for each sign are sent as input to the POS (Part of Speech) tagging module after sign recognition. It is utilized a word net POS tagger with its own word net dictionary. Finally, the sentence is framed using the LALR Parser. In this approach the suggested sign language Interpreter model produces intelligible sentence. This sentence is again converted into audio using gTTS API .We will be developing an android application which will scan the sign languages and interpret it into specific sentence and audio using which a normal person can understand the language of Deaf and Dumb People.

Sentence Formation Using NLP on the Basis of American Sign Language

Abstract: Natural language processing at its core is a method to understand, process and utilize human language that helps in the development of different tools. One such field where this tool can be used for is sign language which is the primary method of communication for the impaired which usually requires a translator to interpret the meaning for those who do not have the knowledge. This paper aims to propose a method that can translate recognized signed words from ASL into proper grammatically correct English sentences with the use of different NLP techniques and parsing it into a Lexicalized Tree Adjoining Grammar (LTAG) using LALR parser. This approach uses a LTAG which is a lexicon that is organized on grammar and vocabulary of the English language and connects in a group of trees. The output matrix of words from the sign recognition is used as an input for the Parts of Speech (POS) tagger that will be parsed into the grammar tree giving a proper English sentence which will be verified by using Language Tool to check the grammar of the final sentence. Keywords: Sentence Formation, American Sign Language, Natural Language Processing, Sign Language recognition, Grammar mapping

NLP-based Sign Gesture Identification for Disabled People

There are many methods for identifying signs, each of which generates a word for each one. It focuses on converting sign language into an appropriate English sentence. NLP techniques are also used in addition to sign recognition. The input is a framed and split video of sign language. This booklet teaches deaf and mute people sign language. It's tough for non-blind persons to engage with blind people due to communication difficulties. To address this issue, the article suggests and describes an effective method. Language technology methods such as POS tagging and the LALR parser are used to convert identified sign words into English phrases. A number of applications are currently on the industry that allow blind people to interact with the world. Combining technology will not be able to address the problem of mobile sign language translation in daily activities. A video interpreter can assist deaf or hearing-impaired people in a variety of situations. People with hearing impairments will be able to learn sign language and have films translated into sign language as a consequence of this research. The present work may be used as a communication interface for both speech-impaired and non-speech-impaired individuals. It will assist bridge the communication gap between speechimpaired people and the rest of the population by capturing and analyzing signals, as well as recognizing and displaying output in the form of comprehensible phrases.

The LALR Parser Implementation using Grammar Rules

Syntactic parsing deals with the syntactic structure of a sentence. It refers to the grammatical arrangement of words in a complete sentence. A syntactic analysis of English words will be presented using bottom-up parsing in which the LALR parser defines the best syntax analysis. A compiler is constructed that generates tokens of the identifiers that are characters, alphabets, etc. using a symbol table, checks for the syntax of sentences by dividing them into phrases, generating errors, debugging them, and giving us the correct sentence.

Visual exploration of visual parser execution

In this paper, we present ParVis, an interactive visual system for the animated visualization of logged parser trace executions. The system allows a parser implementer to create a visualizer for generated parsers by simply defining a JavaScript module that maps each logged parser instruction into a set of events driving the visual system interface. The result is a set of interacting graphical/text windows that allows users to explore logged parser executions and helps them to have a complete understanding of how the parser behaves during its execution on a given input. We used our system to visualize the behavior of textual as well as visual parsers and describe here two of these uses. Moreover, in order to validate the efficacy of our system, we ran a user experiment where students analyzed a CUP-generated parser both with ParVis, instantiated to LALR parsers, and the standard CUP debug facilities. The results show that students can indeed analyze parser behavior and find mistakes in parser specifications more easily and quickly using ParVis. In particular, in some parser design tasks, using ParVis participants achieved a higher success rate of 50% in 42% less time with respect to the baseline system.

Labral Preservation: Outcomes Following Labrum Augmentation Versus Labrum Reconstruction

The purpose of this study was to compare the clinical outcomes and patient satisfaction between patients with previous surgeries who underwent hip labral augmentation versus labral reconstruction surgery. From 2006 to 2014, all patients with previous labral procedures who underwent subsequent labral augmentation by the senior surgeon were included. Patients with joint space ≤2mm and lateral center edge angle <20° and who refused to participate in follow-up were excluded. Patients who underwent labral augmentation, preserving macroscopically healthy native labral tissue and adding iliotibial band graft to increase labral volume, were compared with a matching group (1:2) of patients who underwent labral reconstruction where damaged or absent native labral tissue was replaced by the graft. Hip Outcome Score-Activity of Daily Living (HOS-ADL) was the primary outcome measure. Secondary outcomes included the modified Harris Hip Score (mHHS), HOS for Sports (HOS-Sport), Short Form-12, Western Ontario and McMaster Universities Osteoarthritis Index, and patient satisfaction with outcome. Nonparametric statistics were used to compare groups. Thirty-three patients (12 males, 21 females) who underwent labral augmentation (LA group) were compared with 66 (24 males, 42 females) labral reconstruction patients (LR group). The average age was 29 ± 10years in both groups. Six patients (18%) required revision arthroscopy in the LA group, and 9 patients (14%) in the LR group (P=.563). One patient (3%) in the LA group required a total hip arthroplasty, and 3 patients in the LR group (4.5%) had a total hip arthroplasty (P= .99). Of the remaining 26 patients in the LA group and 53 patients in the LR group, minimum 2-year follow-up was available for 21 (81%) and 51 (96%), respectively. Postoperatively the HOS-ADL, HOS-Sport, mHHS, and Western Ontario and McMaster Universities Osteoarthritis Index were significantly higher in the LA group (P< .05). The percentage of patients who reached minimum clinically important difference was significantly higher in the LA group for HOS-ADL (P= .002) and HOS-Sport (P= .008); however, there was no difference for the mHHS (P= .795). Patient satisfaction was 10 and 8 in the LA group and LR group, respectively (P= .585). In patients with previous procedures, the labral augmentation technique with preservation of macroscopically healthy native labral fibers resulted in significantly better outcomes compared with the segmental labral reconstruction procedure where damaged or previously removed labrum was replaced by a graft. Level III, comparative case series.

A Simple, Possibly Correct LR Parser for C11

The syntax of the C programming language is described in the C11 standard by an ambiguous context-free grammar, accompanied with English prose that describes the concept of “scope” and indicates how certain ambiguous code fragments should be interpreted. Based on these elements, the problem of implementing a compliant C11 parser is not entirely trivial. We review the main sources of difficulty and describe a relatively simple solution to the problem. Our solution employs the well-known technique of combining an LALR(1) parser with a “lexical feedback” mechanism. It draws on folklore knowledge and adds several original aspects, including a twist on lexical feedback that allows a smooth interaction with lookahead; a simplified and powerful treatment of scopes; and a few amendments in the grammar. Although not formally verified, our parser avoids several pitfalls that other implementations have fallen prey to. We believe that its simplicity, its mostly declarative nature, and its high similarity with the C11 grammar are strong informal arguments in favor of its correctness. Our parser is accompanied with a small suite of “tricky” C11 programs. We hope that it may serve as a reference or a starting point in the implementation of compilers and analysis tools.

Interpretation of sign language into English using NLP techniques

Sign language is a way of communication for deaf & dumb. Different sign recognition techniques are there which are giving output in the form of word for recognized sign. The proposed method is focusing on interpretation of sign language in proper English sentence. Different NLP techniques are used in addition to sign recognition. Input is given as video of sign language followed by framing & segmentation on video. CamShift algorithm is used for tracking & P2DHMM for hand tracking. Haar Cascade classifier is used for sign identification. After sign recognition, the continuous words for respective sign are given as input to POS tagging module. Word net POS tagger is used which is having its own WordNet dictionary. At last LALR parser is used to frame the sentence. In this way proposed sign language interpreter model gives the output in meaningful English sentence & with a goodaccuracy.

On different LL and LR parsers used in LLLR parsing

As described in Slivnik (2016), LLLR parsing is a method that parses as much of its input string as possible using the backbone SLL(k) parser and uses small embedded canonical left LR(k) parsers to resolve LL conflicts. Once the LL conflict is resolved, the embedded parser produces the left parse of the substring it has just parsed and passes the control back to the backbone parser together with the information about how the backbone parser should realign its stack as a part of the input has been read by the embedded parser. The LLLR parser produces the left parse of the input string without any backtracking and, if used for a syntax-directed translation, it evaluates semantic actions using the same top-down strategy as the canonical LL(k) parser. In this paper, a more general approach towards LLLR parsing is presented as it is described how any kind of canonical LL(k) or LA(k)LL(k′) parser can be used as the backbone parser and how different kinds of embedded canonical left LR(k) or left LA(k)LR(k′) parsers can be used for LL conflict resolution.

Conversion of Tactile Sign Language into English for Deaf/Dumb Interaction

Natural language is the way of communication for normal human beings which includes spoken language, written and body gestures i.e. head gesture, hand gestures, facial expressions and lip motion etc. On the other hand, speech and hearing-impaired people uses sign language for communication which is not understandable for normal people thus they face problems of communication in society. In this problem interpreters are required but the human interpreters are costly and are not an efficient solution. Thus, there is a need of system which will translate the sign language into normal language which will be understandable by normal. The system proposed and explained in the paper is an efficient solution to this problem. In the system, sign recognition is done using CAMSHIFT and P2DHHM algorithm followed by Haar Cascade Classifier. After sign recognition, the language technology techniques of POS tagging and LALR parser are used to convert recognized sign words into English sentence. Till date no any system has worked on sentence framing. Results shows that this system produces 92% of accurate result which will bridge the gap between impaired and Normal people.

Finding counterexamples from parsing conflicts

Writing a parser remains remarkably painful. Automatic parser generators offer a powerful and systematic way to parse complex grammars, but debugging conflicts in grammars can be time-consuming even for experienced language designers. Better tools for diagnosing parsing conflicts will alleviate this difficulty. This paper proposes a practical algorithm that generates compact, helpful counterexamples for LALR grammars. For each parsing conflict in a grammar, a counterexample demonstrating the conflict is constructed. When the grammar in question is ambiguous, the algorithm usually generates a compact counterexample illustrating the ambiguity. This algorithm has been implemented as an extension to the CUP parser generator. The results from applying this implementation to a diverse collection of faulty grammars show that the algorithm is practical, effective, and suitable for inclusion in other LALR parser generators.

LibDSL: a library for developing embedded domain specific languages in d via template metaprogramming

This paper presents a library called LibDSL that helps the implementer of an embedded domain specific language (EDSL) effectively develop it in D language. The LibDSL library accepts as input some kinds of ``specifications'' of the EDSL that the implementer is going to develop and a D program within which an EDSL source program written by the user is embedded. It produces the front-end code of an LALR parser for the EDSL program and back-end code of the execution engine. LibDSL is able to produce two kinds of execution engines, namely compiler-based and interpreter-based engines, either of which the user can properly choose depending on whether an EDSL program is known at compile time or not. We have implemented the LibDSL system by using template metaprogramming and other advanced facilities such as compile-time function execution of D language. EDSL programs developed by means of LibDSL have a nice integrativeness with the host language.

On the incremental growth and shrinkage of LR goto-graphs

The LR(0) goto-graph is the basis for the construction of parsers for several interesting grammar classes such as LALR and GLR. Early work has shown that even when a grammar is an extension to another, the goto-graph of the first is not necessarily a subgraph of the second. Some authors presented algorithms to grow and shrink these graphs incrementally, but the formal proof of the existence of a particular relation between a given goto-graph and a grown or shrunk counterpart seems to be still missing in literature as of today. In this paper we use the recursive projection of paths of limited length to prove the existence of one such relation, when the sets of productions are in a subset relation. We also use this relation to present two algorithms (Grow and Shrink) that transform the goto-graph of a given grammar into the goto-graph of an extension or a restriction to that grammar. We implemented these algorithms in a dynamically updatable LALR parser generator called DEXTER (the Dynamically EXTEnsible Recognizer) that we are now shipping with our current implementation of the Neverlang framework for programming language development.

A Practical GLR Parser Generator for Software Reverse Engineering

Traditional parser generators use deterministic parsing methods. These methods can not meet the parsing requirements of software reverse engineering effectively. A new parser generator is presented which can generate GLR parser with automatic error recovery. The generated GLR parser has comparable parsing speed with the traditional LALR(1) parser and can be used in the parsing of software reverse engineering.

An LR Parser with Less States

This paper presents a new construction method of LR parser which is based on the theory of deterministic finite automata. The deterministic infinite automata are simplified for obtaining the LR parsing table with fewer states by merging the equivalent states. The new construction method has the same reorganization power as LALR parser but avoids the “reduce/reduce” conflict in LALR method. The experiment results show that the number of states in parsing table can be reduced greatly if the proposed method is used.

Jaccie: A Java-based compiler–compiler for generating, visualizing and debugging compiler components

Many programmers live in happy ignorance of their compilers’ internal workings. Others may want to take a look at what is going on inside a compiler in much the same way that they use a debugger to watch their compiled programs execute. While conventional compilers are black boxes whose internals are hidden from the user, the Jaccie tool set helps to open up the box and have a look at what is going on inside.Technically speaking, Jaccie is a compiler–compiler that, from suitable formal descriptions, generates the scanner, parser, and attribute evaluator components of a compiler and presents them in a visual debugging environment. It offers a number of alternative parser generators producing both top-down (LL) and bottom-up parsers of the LR variety, including SLR(1) and LALR(1) parsers, thus allowing users to experiment with different parsing strategies and to get a “feel” for their relative pros and cons. When designing Jaccie, the main emphasis was on two ergonomic goals that we considered important for educational software. Firstly, give user control over the program and not vice versa, e.g., our parsers (and other components) can be directed to go step-by-step forwards or backwards or to leap to some point in the input indicated by the users. Secondly, overcome the sometimes severe size limitations of computer displays by offering the same information in multiple representations that complement each other and by dividing information in smaller chunks that can be traversed in a meaningful way.In this paper, after outlining the architecture of Jaccie, we discuss some of its technical and ergonomic aspects in detail, give a brief introduction into the use of Jaccie and its documentation, show an example application done with Jaccie, and finally discuss related work and future plans.

LR(1) parser generation system

The LR(1) Parser Generation System generates full LR(1) parsers that are comparable in speed and size to those generated by LALR(1) parser generators, such as yacc [5]. In addition to the inherent advantages of full LR(1) parsing, it contains a number of novel features. This paper discusses three of them in detail: an LR(1) grammar specified automatic error recovery algorithm, oracles, and generic tokens. The error recovery algorithm depends on the fact that full LR(1) parse tables preserve context. Oracles are pieces of code that are defined in a grammar and that are executed between the scanner and parser. They are used to resolve token ambiguities, including semantic ones. Generic tokens are used to replace syntactically identical tokens with a single token, which is, in effect, a variable representing a set of tokens.

Automatic detection and replacement of syntactic constructs causing shift/reduce conflicts

AbstractA method is described for repairing some shift/reduce conflicts caused by limited lookahead in LALR(1) parsers such as those built by bison. Also, six types of Extended BNF (EBNF) construct are identified that cause a shift/reduce conflict when a Yet Another Compiler Compiler (YACC) file translated directly from EBNF is processed by bison. For each type, a replacement EBNF construct is described representing the same grammar and causing no shift/reduce conflict when its YACC equivalent is processed by bison. Algorithms are given for identifying instances of each type and transforming them into their replacements. The algorithms are implemented in an automatic parser builder that builds parsers for subsets of the DMIS language. The parser builder reads an EBNF file and writes C++ classes, a YACC file, and a Lex file, which are then processed to build a parser. The parsers build a parse tree using the C++ classes. The EBNF for DMIS is written in natural terms so that natural C++ classes are generated. However, if translated directly into YACC, the natural EBNF leads to 22 shift/reduce conflicts that fall into the six types. The parser builder recognizes the six constructs and replaces them automatically before generating YACC. The YACC that is generated parses in terms of unnatural constructs while building a parse tree using natural C++ classes. The six types of construct may occur in any statement‐based language that uses a minor separator, such as a comma; hence knowing how to recognize and replace them may be broadly useful. Published in 2010 by John Wiley &amp; Sons, Ltd.

An LALR Parser Generator Supporting Conflict Resolution

An LALR Parser Generator Supporting Conflict Resolution

States merging in LR parser

Here a scheme is presented, which reduces the size of an LR parser. It is accomplished by merging two states, which do not perform differently on the same input symbol or non-terminal. This state merging has following advantages:1. The reduction in number of states is greater than the reduction in an LALR parser.2. The power of parser is not reduced as it would be in an LALR parser.The above merging scheme has following disadvantages:1. Even erroneous inputs are accepted. However, it is stopped by changing the notion of acceptance.2. Errors are report late and in a misleading manner.3. Parsing takes more time because string matching is done during reduction.